Indonesian Journal of Electrical Engineering and Computer Science
Detection and classification of brain tumors are of formidable importance in neuroscience. Deep l... more Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our...
With the progressive development of a wide range of applications, interconnect things and interne... more With the progressive development of a wide range of applications, interconnect things and internet of things (IoT) became an imperative required trend by industries and academicians. IoT became a base infrastructure for remote access or control depending on internet protocol (IP) networks, especially after the COVID-19 pandemic. The huge application domain’s infrastructure, which depends on IoT, requires a trusted connection to guarantee security and privacy while transferring data. This paper proposes a hybrid identity authentication pipeline that integrates three schemes, namely, an elliptic curve cryptography (ECC) scheme is integrated with the Ong, Schnorr, and Shamir (OSS) signature scheme and chaotic maps. The latter satisfies both security and guarantee criteria. The novelty of the proposal is in using chaotic mapping and a cyclic group to deduce a substitution box (S-Box) and a reversible matrix as a portion of the OSS signature equation. The ECC-based security part is an ef...
Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It i... more Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a ...
Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropr... more Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated...
Background: There is an urgent need for a reliable in vitro system that can accurately replicate ... more Background: There is an urgent need for a reliable in vitro system that can accurately replicate the cardiac physiological environment for drug testing. The limited availability of human heart tissue culture systems has led to inaccurate interpretations of cardiac related drug effects. Our group had previously developed a culture system for pig/human heart slices that enables functional and structural viability for 6 days under electric stimulation and enriched media and can reliably demonstrate the subacute toxic effects of anti-cancer therapeutics. However, by day 10, the slices undergo cardiomyocyte dedifferentiation and fibrotic remodeling, making them inadequate for long-term drug testing.Aims: To determine whether including physiological mechanical and humoral cues within the culture system can prolong the viability of the heart tissue in culture.Methods and Results: We have developed a novel cardiac tissue culture model (CTCM) that can electro-mechanically stimulate heart sli...
Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage ... more Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov–Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification ...
2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV... more A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV) is proposed in this paper. The method is composed of two steps. First, a fully convolutional U-net is used for the segmentation of the epi- and endo-cardial boundaries of the LV from cine MR images. This step incorporates a novel loss function that accounts for the class imbalance problem caused by the binary cross entropy (BCE) loss function. Our novel loss function maximizes the segmentation accuracy and penalizes the effect of the class-imbalance caused by BCE. In the second step, the ventricular volume curves are constructed from which LV function parameter is estimated (i.e., ejection fraction). Our method demonstrated a statistical significance in the segmentation of the epi- and endo-cardial boundaries (Dice score of 0.94 and 0.96, respectively) compared with the BCE loss (Dice score of 0.89 and 0.86, respectively). Furthermore, a high positive correlation of 0.97 between the estimated ejection fraction and the gold standard was obtained.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Achieving early detection and classification of thyroid nodules contributes to the prediction of ... more Achieving early detection and classification of thyroid nodules contributes to the prediction of cancer burdening and also steers appropriate clinical pathways of that medical condition. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that detects cancerous thyroid nodules using a deep-learning architecture. Particularly, our system is built with a multi-input convolutional neural network (CNN) to perform fusion of two MRI modalities: the diffusion weighted image (DWI) and apparent diffusion coefficient (ADC) map. The main contribution of our system is three-folded. Namely, (1) it is the first system to fuse thyroid DWI and ADC using CNN for classification purpose; (2) it enables independent convolutions process for each of DWI and ADC images, which can increase the likelihood of detecting deep texture patterns in thyroid nodules; and (3) it enables adding extra channels in each input with the possibility to integrate with additional MRI modalities and other imaging technologies. We compared our system to other fusion methods and also to other machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among them with diagnostic accuracy of 0.88, precision of 0.82, and recall of 0.82.
Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) nece... more Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) necessitate important prerequisites, such as secrecy, legitimacy, and integrity. This paper recommends a novel hybrid encryption/decryption scheme that can be applied in e-healthcare, or IoHS, for the protection of medical images. The proposed system explores innovative perturbation algorithms that utilize novel chaotic maps. The proposed perturbation-based data encryption is employed in both rounds of confusion and diffusion to cope the drawbacks of traditional chaos-based confusion and diffusion architectures. Particularly, (i) the new maps parameters and chaotic sequences are used to control the permutation and diffusion properties of the scheme, and (2) the derived properties control pixel shuffling and operations of substitution. Different techniques and tests are used to analyzed the chaotic behaviors of the proposed system, including the bifurcation diagram, Lyapunov exponents, as well as the NIST and DIEHARD tests. Moreover, evaluation using various test images indicated that the proposed cryptosystem is fast, have high efficiency, showed high robustness and protection of medical images, and documented the good ability to withstand a variety of cyber-attacks. Furthermore, quantitative results using benchmark color and greyscale images prove the high security levels, sensitivity, and low residual intelligibility with high quality recovered data of our technique than several typical and state-of-the-art encryption schemes. This has been documented using statistical and security analysis metrics, such as number of pixels change rate (NPCR, 99.814%), unified average changing intensity (UACI, 33.694%), peak signal-to-noise-ratio (PSNR, 7.723), and Shannon entropy (7.998).
2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clini... more Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clinical condition. Guidelines recognize two categories of MI: Thrombotic (Type 1) and non-thrombotic (Type 2), that have quite same prevalence but require unlike treatment. Unfortunately, diagnostic criteria to differentiate between Type 1 and Type 2 require invasive procedures. This results in inefficient and sub-optimal care of patients suspected of MI. This paper presents a novel machine-learning system that detects biomarkers of thrombus formation by analyzing the association between plasma metabolites with the formation of thrombosis in cohort of MI patients at multiple time-points. Study data are collected by a newly introduced non-targeted technique that evaluates the quantities of both known and unknown metabolites from blood samples. Our system uses recursive feature elimination (RFE) and multi-layer perceptron (MLP) neural network to detect associated metabolites at each time-point followed by weighted-voting algorithm using ensemble learning. Our experiment achieves an accuracy of 91%, sensitivity of 89%, and specificity of 94% for MI diagnosis.
2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), 2019
Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clin... more Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clinical diagnosis. Current guidelines recognize two classes of MI: thrombotic (Type 1) and non-thrombotic (Type 2), which have similar prevalence but require different treatment. Unfortunately, diagnostic criteria for differentiating between the two do not exist. This results in inefficient and sub-optimal care of patients suspected of MI. This paper introduces a novel CAD system for categorizing MI by analyzing the association between blood metabolites with the formation of thrombosis as a cause of MI. The data collection stage involves a new non-targeted approach that detects the quantities of both know and unknown metabolites from blood samples. Our system uses a randomized ensemble-learning scheme to increase the model accuracy. This scheme includes a recursive feature elimination (RFE) algorithm to discard weakly correlated metabolites and a multi-classifier algorithm that votes between five different classifiers in order to enhance accuracy. The output of our system includes suggested clusters of metabolites that act as biomarkers to differentiate between Type 1 and Type 2 MI. Our experiment achieves diagnostic accuracy of %94, sensitivity of %90 and specificity of %81.
2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018
The goal of this paper is to determine the parameters that are correlated with the biopsy diagnos... more The goal of this paper is to determine the parameters that are correlated with the biopsy diagnosis of acute renal rejection (AR) post-transplantation, using laboratory biomarkers and (3D+ -value) diffusion weighted MR (DW-MR) image- markers. 16 patients with non-rejection (NR) and 45 patients with AR renal allografts determined by their renal biopsy as a gold standard were included. All kidneys were evaluated using both laboratory biomarkers (e.g., creatinine clearance (CrCl) and serum creatinine (SCr)) and DW-MR image-markers. To extract the latter, DW-MR kidney images were first segmented using a geometric deformable model, then, DW-MR image-markers known as apparent diffusion coefficients (ADCs), were estimated for segmented kidneys at multiple b-values (i.e. strength and timing, of, the, field, gradients, (b50,b 100,,…,b 1000 s/mm2)). A statistical analysis investigating possible correlations between potential biomarkers of AR and the biopsy diagnosis was firstly performed. Two...
Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening a... more Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the pro...
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Left ventricular (LV) volumes, and emptying and filling function remain important indices in cond... more Left ventricular (LV) volumes, and emptying and filling function remain important indices in conditions such as heart failure. These parameters are derived from the volume curve contained by the inner border of the LV of the heart, throughout the emptying and filling phases of the cardiac cycle, and the peak emptying and filling rates. The gold standard uses the Simpson rule to estimate volume from stacks of short axis images acquired using cine MRI. In this study, a deep learning, automated supervised approach to estimate ventricular volumes is introduced. Unlike prior methods that required hand-crafted image features to segment the inner contour, the proposed approach uses an automatically selected region of interest (ROI), and intelligently determines the optimum features directly from the ROI information. These derived features are then inputted into a deep learning regression model, with the estimated volume as the output results.
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Bladder cancer is the most prevalent urinary tract malignancy and is associated with the highest ... more Bladder cancer is the most prevalent urinary tract malignancy and is associated with the highest treatment cost among cancers. The standard therapy for patients with muscle-invasive bladder cancer is the radical cystectomy. Nevertheless, there is a high mortality rate within five years after cystectomy. Neoadjuvant chemotherapy of cisplatin-based combinations have been shown to increase the overall survival of the patients. However, not all patients get benefits from the chemotherapy due to resistance to cisplatin, and there is no reliable biomarker to identify the non-responders. Therefore there is a need to identify chemoresistant patients, by finding cisplatin-response biomarkers, to avoid unnecessary surgery delay and drug toxicity. In this paper, we present a review of the current predictive biomarkers to neoadjuvant chemotherapy response that aim to provide a way of individualized therapy in muscle invasive bladder cancer. Furthermore, we will discuss the effectiveness and limitations of these biomarkers. Finally, future trends in this area of research will be discussed.
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Bladder cancer (BC) is the seventh most prevalent cancer in the United States. Accurate detection... more Bladder cancer (BC) is the seventh most prevalent cancer in the United States. Accurate detection and diagnosis of BC helps clinicians to select appropriate treatment and thus increase patients’ survival rate. Multiple factors affect accurate diagnosis, including cancer staging and grading, lymph node involvement, and metastasis. The use of medical imaging techniques has been widely explored to help clinicians/physicians to more accurately localize and diagnose BC. Particularly, magnetic resonance imaging (MRI) has been recently found to play a crucial role in BC diagnosis and staging. This paper provides an overview of recent MRI studies for BC detection and diagnosis. Namely, the paper discusses recent MRI studies for BC diagnosis and staging using different MRI-derived markers (e.g. wash-in and wash-out rates, apparent diffusion coefficient(ADC)). Challenges and limitations of recent literature works and recommendation for future research paths will also be discussed.
Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia... more Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion and diffusion rounds is proposed. Our chaotification structure is hybrid, in which multiple maps are combined combines for media encryption. Blended chaotic maps are used to generate the control parameters for the permutation (shuffling) and diffusion (substitution) structures. The proposed schemes not only maintain great encryption quality reproduced by chaotic, but also possess other advantages, including key sensitivity and low residual clarity. Extensive security and differential analyses documented that the proposed schemes are efficient for secure multimedia transmission as well as the encrypted media possesses resistance to attacks. Add...
Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In... more Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).
Indonesian Journal of Electrical Engineering and Computer Science
Detection and classification of brain tumors are of formidable importance in neuroscience. Deep l... more Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our...
With the progressive development of a wide range of applications, interconnect things and interne... more With the progressive development of a wide range of applications, interconnect things and internet of things (IoT) became an imperative required trend by industries and academicians. IoT became a base infrastructure for remote access or control depending on internet protocol (IP) networks, especially after the COVID-19 pandemic. The huge application domain’s infrastructure, which depends on IoT, requires a trusted connection to guarantee security and privacy while transferring data. This paper proposes a hybrid identity authentication pipeline that integrates three schemes, namely, an elliptic curve cryptography (ECC) scheme is integrated with the Ong, Schnorr, and Shamir (OSS) signature scheme and chaotic maps. The latter satisfies both security and guarantee criteria. The novelty of the proposal is in using chaotic mapping and a cyclic group to deduce a substitution box (S-Box) and a reversible matrix as a portion of the OSS signature equation. The ECC-based security part is an ef...
Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It i... more Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a ...
Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropr... more Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated...
Background: There is an urgent need for a reliable in vitro system that can accurately replicate ... more Background: There is an urgent need for a reliable in vitro system that can accurately replicate the cardiac physiological environment for drug testing. The limited availability of human heart tissue culture systems has led to inaccurate interpretations of cardiac related drug effects. Our group had previously developed a culture system for pig/human heart slices that enables functional and structural viability for 6 days under electric stimulation and enriched media and can reliably demonstrate the subacute toxic effects of anti-cancer therapeutics. However, by day 10, the slices undergo cardiomyocyte dedifferentiation and fibrotic remodeling, making them inadequate for long-term drug testing.Aims: To determine whether including physiological mechanical and humoral cues within the culture system can prolong the viability of the heart tissue in culture.Methods and Results: We have developed a novel cardiac tissue culture model (CTCM) that can electro-mechanically stimulate heart sli...
Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage ... more Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov–Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification ...
2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV... more A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV) is proposed in this paper. The method is composed of two steps. First, a fully convolutional U-net is used for the segmentation of the epi- and endo-cardial boundaries of the LV from cine MR images. This step incorporates a novel loss function that accounts for the class imbalance problem caused by the binary cross entropy (BCE) loss function. Our novel loss function maximizes the segmentation accuracy and penalizes the effect of the class-imbalance caused by BCE. In the second step, the ventricular volume curves are constructed from which LV function parameter is estimated (i.e., ejection fraction). Our method demonstrated a statistical significance in the segmentation of the epi- and endo-cardial boundaries (Dice score of 0.94 and 0.96, respectively) compared with the BCE loss (Dice score of 0.89 and 0.86, respectively). Furthermore, a high positive correlation of 0.97 between the estimated ejection fraction and the gold standard was obtained.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Achieving early detection and classification of thyroid nodules contributes to the prediction of ... more Achieving early detection and classification of thyroid nodules contributes to the prediction of cancer burdening and also steers appropriate clinical pathways of that medical condition. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that detects cancerous thyroid nodules using a deep-learning architecture. Particularly, our system is built with a multi-input convolutional neural network (CNN) to perform fusion of two MRI modalities: the diffusion weighted image (DWI) and apparent diffusion coefficient (ADC) map. The main contribution of our system is three-folded. Namely, (1) it is the first system to fuse thyroid DWI and ADC using CNN for classification purpose; (2) it enables independent convolutions process for each of DWI and ADC images, which can increase the likelihood of detecting deep texture patterns in thyroid nodules; and (3) it enables adding extra channels in each input with the possibility to integrate with additional MRI modalities and other imaging technologies. We compared our system to other fusion methods and also to other machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among them with diagnostic accuracy of 0.88, precision of 0.82, and recall of 0.82.
Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) nece... more Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) necessitate important prerequisites, such as secrecy, legitimacy, and integrity. This paper recommends a novel hybrid encryption/decryption scheme that can be applied in e-healthcare, or IoHS, for the protection of medical images. The proposed system explores innovative perturbation algorithms that utilize novel chaotic maps. The proposed perturbation-based data encryption is employed in both rounds of confusion and diffusion to cope the drawbacks of traditional chaos-based confusion and diffusion architectures. Particularly, (i) the new maps parameters and chaotic sequences are used to control the permutation and diffusion properties of the scheme, and (2) the derived properties control pixel shuffling and operations of substitution. Different techniques and tests are used to analyzed the chaotic behaviors of the proposed system, including the bifurcation diagram, Lyapunov exponents, as well as the NIST and DIEHARD tests. Moreover, evaluation using various test images indicated that the proposed cryptosystem is fast, have high efficiency, showed high robustness and protection of medical images, and documented the good ability to withstand a variety of cyber-attacks. Furthermore, quantitative results using benchmark color and greyscale images prove the high security levels, sensitivity, and low residual intelligibility with high quality recovered data of our technique than several typical and state-of-the-art encryption schemes. This has been documented using statistical and security analysis metrics, such as number of pixels change rate (NPCR, 99.814%), unified average changing intensity (UACI, 33.694%), peak signal-to-noise-ratio (PSNR, 7.723), and Shannon entropy (7.998).
2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019
Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clini... more Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clinical condition. Guidelines recognize two categories of MI: Thrombotic (Type 1) and non-thrombotic (Type 2), that have quite same prevalence but require unlike treatment. Unfortunately, diagnostic criteria to differentiate between Type 1 and Type 2 require invasive procedures. This results in inefficient and sub-optimal care of patients suspected of MI. This paper presents a novel machine-learning system that detects biomarkers of thrombus formation by analyzing the association between plasma metabolites with the formation of thrombosis in cohort of MI patients at multiple time-points. Study data are collected by a newly introduced non-targeted technique that evaluates the quantities of both known and unknown metabolites from blood samples. Our system uses recursive feature elimination (RFE) and multi-layer perceptron (MLP) neural network to detect associated metabolites at each time-point followed by weighted-voting algorithm using ensemble learning. Our experiment achieves an accuracy of 91%, sensitivity of 89%, and specificity of 94% for MI diagnosis.
2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), 2019
Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clin... more Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clinical diagnosis. Current guidelines recognize two classes of MI: thrombotic (Type 1) and non-thrombotic (Type 2), which have similar prevalence but require different treatment. Unfortunately, diagnostic criteria for differentiating between the two do not exist. This results in inefficient and sub-optimal care of patients suspected of MI. This paper introduces a novel CAD system for categorizing MI by analyzing the association between blood metabolites with the formation of thrombosis as a cause of MI. The data collection stage involves a new non-targeted approach that detects the quantities of both know and unknown metabolites from blood samples. Our system uses a randomized ensemble-learning scheme to increase the model accuracy. This scheme includes a recursive feature elimination (RFE) algorithm to discard weakly correlated metabolites and a multi-classifier algorithm that votes between five different classifiers in order to enhance accuracy. The output of our system includes suggested clusters of metabolites that act as biomarkers to differentiate between Type 1 and Type 2 MI. Our experiment achieves diagnostic accuracy of %94, sensitivity of %90 and specificity of %81.
2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018
The goal of this paper is to determine the parameters that are correlated with the biopsy diagnos... more The goal of this paper is to determine the parameters that are correlated with the biopsy diagnosis of acute renal rejection (AR) post-transplantation, using laboratory biomarkers and (3D+ -value) diffusion weighted MR (DW-MR) image- markers. 16 patients with non-rejection (NR) and 45 patients with AR renal allografts determined by their renal biopsy as a gold standard were included. All kidneys were evaluated using both laboratory biomarkers (e.g., creatinine clearance (CrCl) and serum creatinine (SCr)) and DW-MR image-markers. To extract the latter, DW-MR kidney images were first segmented using a geometric deformable model, then, DW-MR image-markers known as apparent diffusion coefficients (ADCs), were estimated for segmented kidneys at multiple b-values (i.e. strength and timing, of, the, field, gradients, (b50,b 100,,…,b 1000 s/mm2)). A statistical analysis investigating possible correlations between potential biomarkers of AR and the biopsy diagnosis was firstly performed. Two...
Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening a... more Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the pro...
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Left ventricular (LV) volumes, and emptying and filling function remain important indices in cond... more Left ventricular (LV) volumes, and emptying and filling function remain important indices in conditions such as heart failure. These parameters are derived from the volume curve contained by the inner border of the LV of the heart, throughout the emptying and filling phases of the cardiac cycle, and the peak emptying and filling rates. The gold standard uses the Simpson rule to estimate volume from stacks of short axis images acquired using cine MRI. In this study, a deep learning, automated supervised approach to estimate ventricular volumes is introduced. Unlike prior methods that required hand-crafted image features to segment the inner contour, the proposed approach uses an automatically selected region of interest (ROI), and intelligently determines the optimum features directly from the ROI information. These derived features are then inputted into a deep learning regression model, with the estimated volume as the output results.
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Bladder cancer is the most prevalent urinary tract malignancy and is associated with the highest ... more Bladder cancer is the most prevalent urinary tract malignancy and is associated with the highest treatment cost among cancers. The standard therapy for patients with muscle-invasive bladder cancer is the radical cystectomy. Nevertheless, there is a high mortality rate within five years after cystectomy. Neoadjuvant chemotherapy of cisplatin-based combinations have been shown to increase the overall survival of the patients. However, not all patients get benefits from the chemotherapy due to resistance to cisplatin, and there is no reliable biomarker to identify the non-responders. Therefore there is a need to identify chemoresistant patients, by finding cisplatin-response biomarkers, to avoid unnecessary surgery delay and drug toxicity. In this paper, we present a review of the current predictive biomarkers to neoadjuvant chemotherapy response that aim to provide a way of individualized therapy in muscle invasive bladder cancer. Furthermore, we will discuss the effectiveness and limitations of these biomarkers. Finally, future trends in this area of research will be discussed.
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2018
Bladder cancer (BC) is the seventh most prevalent cancer in the United States. Accurate detection... more Bladder cancer (BC) is the seventh most prevalent cancer in the United States. Accurate detection and diagnosis of BC helps clinicians to select appropriate treatment and thus increase patients’ survival rate. Multiple factors affect accurate diagnosis, including cancer staging and grading, lymph node involvement, and metastasis. The use of medical imaging techniques has been widely explored to help clinicians/physicians to more accurately localize and diagnose BC. Particularly, magnetic resonance imaging (MRI) has been recently found to play a crucial role in BC diagnosis and staging. This paper provides an overview of recent MRI studies for BC detection and diagnosis. Namely, the paper discusses recent MRI studies for BC diagnosis and staging using different MRI-derived markers (e.g. wash-in and wash-out rates, apparent diffusion coefficient(ADC)). Challenges and limitations of recent literature works and recommendation for future research paths will also be discussed.
Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia... more Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion and diffusion rounds is proposed. Our chaotification structure is hybrid, in which multiple maps are combined combines for media encryption. Blended chaotic maps are used to generate the control parameters for the permutation (shuffling) and diffusion (substitution) structures. The proposed schemes not only maintain great encryption quality reproduced by chaotic, but also possess other advantages, including key sensitivity and low residual clarity. Extensive security and differential analyses documented that the proposed schemes are efficient for secure multimedia transmission as well as the encrypted media possesses resistance to attacks. Add...
Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In... more Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).
Background: Previous reports indicate the presence of histological abnormalities in the brains of... more Background: Previous reports indicate the presence of histological abnormalities in the brains of individuals with autism spectrum disorders (ASD) suggestive of a dysplastic process. In this study we identified areas of abnormal cortical thinning within the cerebral cortex of ASD individuals and examined the same for neuronal morphometric abnormalities by using computerized image analysis. Results: The study analyzed celloidin-embedded and Nissl-stained serial full coronal brain sections of 7 autistic (ADI-R diagnosed) and 7 age/sex-matched neurotypicals. Sections were scanned and manually segmented before implementing an algorithm using Laplace's equation to measure cortical width. Identified areas were then subjected to analysis for neuronal morphometry. Results of our study indicate the presence within our ASD population of circumscribed foci of diminished cortical width that varied among affected individuals both in terms of location and overall size with the frontal lobes being particularly involved. Spatial statistic indicated a reduction in size of neurons within affected areas. Granulometry confirmed the presence of smaller pyramidal cells and suggested a concomitant reduction in the total number of interneurons. Conclusions: The neuropathology is consistent with a diagnosis of focal cortical dysplasia (FCD). Results from the medical literature (e.g., heterotopias) and our own study suggest that the genesis of this cortical malformation seemingly resides in the heterochronic divisions of periventricular germinal cells. The end result is that during corticogenesis radially migrating neuroblasts (future pyramidal cells) are desynchronized in their development from those that follow a tangential route (interneurons). The possible presence of a pathological mechanism in common among different conditions expressing an autism-like phenotype argue in favor of considering ASD a "sequence" rather than a syndrome. Focal cortical dysplasias in ASD may serve to explain the high prevalence of seizures and sensory abnormalities in this patient population.
MRI studies on abnormal brain development are dependent on the quality, quantity, and type of nor... more MRI studies on abnormal brain development are dependent on the quality, quantity, and type of normative development data available for comparison. Limitations affecting previous studies on normative development include small sample sizes, lack of demographic representation, heterogeneous subject populations, and inadequate longitudinal data. The National Institutes of Health Pediatric MRI Data Repository (NIHPD) for normative development was designed to address the aforementioned issues in reliability measures of control subjects for comparison studies. The subjects were recruited from six Pediatric Study Centers nationwide to create the largest, non-biased, longitudinal database of the developing brain. Using the NIHPD, we applied a 3D shape analysis method involving spherical harmonics to identify the cortical surface complexity of 396 subjects (210 female; 186 male) between the ages of 4.8 y and 22.3 y. MRI data had been obtained at one, two, or three time points approximately two years apart. A total of 144 participants (79 female; 65 male) provided MRI data from all time points. Our results confirm a direct correlation between cortical complexity and age in both males and females. Additionally, within the examined age range, females displayed consistently and significantly greater cortical complexity than males. Findings suggest that the underlying neural circuitry within male and female brains is different, possibly explaining observations of sexual dimorphism in social interaction, communication, and higher cognitive processes.
The authors propose 3D (2D + time) novel, fast, robust, bidirectional coupled parametric deformab... more The authors propose 3D (2D + time) novel, fast, robust, bidirectional coupled parametric deformable models that are capable of segmenting left ventricle (LV) wall borders using first- and second-order visual appearance features. The authors examine the effect of the proposed segmentation method on the estimation of global cardiac performance indexes. First-order visual appearance of the cine cardiac magnetic resonance (CMR) signals (inside and outside the boundary of the deformable model) is modeled with an adaptive linear combination of discrete Gaussians (LCDG). Second-order visual appearance of the LV wall is accurately modeled with a translational and rotation-invariant second-order Markov-Gibbs random field (MGRF). The LCDG parameters are estimated using our previously proposed modification of the EM algorithm, and the potentials of rotationally invariant MGRF are computed analytically. The authors tested the proposed segmentation approach on 15 cine CMR data sets using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. The authors documented an average DSC value of 0.926 ± 0.022 and an average AD value of 2.16 ± 0.60 mm compared to two other level set methods that achieve an average DSC values of 0.904 ± 0.033 and 0.885 ± 0.02; and an average AD values of 2.86 ± 1.35 mm and 5.72 ± 4.70 mm, respectively. The proposed segmentation approach demonstrated superior performance over other methods. Specifically, the comparative results on the publicly available MICCAI 2009 Cardiac MR Left Ventricle Segmentation database documented superior performance of the proposed approach over published methods. Additionally, the high accuracy of our segmentation approach leads to accurate estimation of the global performance indexes, as evidenced by the Bland-Altman analyses of the end-systolic volume (ESV), end-diastolic volume (EDV), and the ejection fraction (EF) ratio.
Problem of non-rigid registration has become very important in the area of biomedical imaging. A ... more Problem of non-rigid registration has become very important in the area of biomedical imaging. A non-rigid registration problem is modeled as an optimization problem and is solved using graph cuts and MRFs in recent years. In this paper, we have improved the graph cuts-based solution to non-rigid registration with a novel data term. The proposed data term has several advantages. Firstly, displacement labels can be directly assigned from this data term. Secondly, our data term imposes stricter penalty for intensity mismatches and hence yields higher registration accuracy. Finally, this data term can efficiently handle the dissimilarities in the intensity patterns between the floating and the reference images which may also arise due to some changes in illumination in addition to motion. We show the effectiveness of the proposed method on MRI images of brain and light microscopy images of retina. Experimental results indicate the superiority of our technique over some well-known non-rigid registration algorithms.
Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-fli... more Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussianapproximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.
Acquisition of noncontrast agent cine cardiac magnetic resonance (CMR) gated images through the c... more Acquisition of noncontrast agent cine cardiac magnetic resonance (CMR) gated images through the cardiac cycle is, at present, a well-established part of examining cardiac global function. However, regional quantification is less well established. We propose a new automated framework for analyzing the wall thickness and thickening function on these images that consists of three main steps. First, inner and outer wall borders are segmented from their surrounding tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. In the second step, point-to-point correspondences between the inner and outer borders are found by solving the Laplace equation and provide initial estimates of the local wall thickness and the thickening function index. Finally, the effects of the segmentation error is reduced and a continuity analysis of the LV wall thickening is performed through iterative energy minimization using a generalized Gauss-Markov random field (GGMRF) image model. The framework was evaluated on 26 datasets from clinical cine CMR images that have been collected from patients with eleven independent studies, with chronic ischemic heart disease and heart damage. The performance evaluation of the proposed segmentation approach, based on the receiver operating characteristic (ROC) and Dice similarity coefficients (DSC) between manually drawn and automatically segmented contours, confirmed a high robustness and accuracy of the proposed segmentation approach. Furthermore, the Bland-Altman plot is used to assess the limit of agreement of our measurements of the global function parameters compared to the ground truth. Importantly, comparative results on the publicly available database (MICCAI 2009 Cardiac MR Left Ventricle Segmentation) demonstrated a superior performance of the proposed segmentation approach over published methods.
Objective: The aim was to evaluate the effects of diagnostic performance of diffusionweighted (DW... more Objective: The aim was to evaluate the effects of diagnostic performance of diffusionweighted (DW) MRI in the assessment of acute impairment of transplanted kidneys. patients with stable renal allograft function (Group 1) and 21 patients with acute graft impairment (Group 2) were included in the study. All patients were evaluated with coronal T 2 weighted (T 2 W) and DW MRI of the kidney. Patients in Group 2 underwent graft biopsy to determine the underlying histopathological aetiology. Apparent diffusion coefficient (ADC) was calculated and the kidneys were studied for any areas of diffusion restriction. Two radiologists, who were blinded to the results of histopathology, independently interpreted the T 2 W and DW images. Results: The histopathological diagnosis ofGroup 2 (21 patients) was acute cellular rejection (ACR) in 10, acute tubular necrosis (ATN) in 7 and immunosuppressive toxicity in 4 patients. ADC values in Group 1 were significantly higher compared with Group 2 (p,0.001), patients with ACR (p,0.001), patients with ATN (p,0.001) and patients with drug toxicity (p,0.001). Using 2610 23 mm 2 s 21 as a cut-off, there was no overlap between the ADC values of patients with normal graft function and those with ATN. Both ACR and ATN had a low ADC value, but on the ADC map the kidney in cases of ATN appears heterogeneous with a characteristic mosaic pattern resembling the Tiger skin. There was no significant T 2 W morphological difference between the two groups. Conclusion: These results show how DW MRI is a promising new technique for the diagnosis of acute renal transplant dysfunction.
This paper overviews one of the most important, interesting, and challenging problems in oncology... more This paper overviews one of the most important, interesting, and challenging problems in oncology, early diagnosis of prostate cancer. Developing effective diagnostic techniques for prostate cancer is of great clinical importance and can improve the effectiveness of treatment and increase the patient's chance of survival. The main focus of this study is to overview the different in-vitro and in-vivo technologies for diagnosing prostate cancer. This review discusses the current clinically used in-vitro cancer diagnostic tools, such as biomarker tests and needle biopsies and including their applications, advantages, and limitations. Moreover, the current in-vitro research tools that focus on the role of nanotechnology in prostate cancer diagnosis have been detailed. In addition to the in-vitro techniques, the current study discusses in detail developed in-vivo non-invasive state-of-the-art Computer-Aided Diagnosis (CAD) systems for prostate cancer based on analyzing Transrectal Ultrasound (TRUS) and different types of magnetic resonance imaging (MRI), e.g., T 2 -MRI, Diffusion Weighted Imaging (DWI), Dynamic Contrast Enhanced (DCE)-MRI, and multi-parametric MRI, focusing on their implementation, experimental procedures, and reported outcomes. Furthermore, the paper addresses the limitations of the current prostate cancer diagnostic techniques, outlines the challenges that these techniques face, and introduces the recent trends to solve these challenges, which include biomarkers used in in-vitro lab-on-a-chip nanotechnology-based methods.
To develop an automated framework for accurate analysis of myocardial perfusion using first-pass ... more To develop an automated framework for accurate analysis of myocardial perfusion using first-pass magnetic resonance imaging. The proposed framework consists of four processing stages. First, in order to account for heart deformations due to respiratory motion and heart contraction, a two-step registration methodology is proposed, which has the ability to account for the global and local motions of the heart. The methodology involves an affine-based registration followed by a local B-splines alignment to maximize a new similarity function based on the first- and second-order normalized mutual information. Then the myocardium is segmented using a level-set function, its evolution being constrained by three features, namely, a weighted shape prior, a pixelwise mixed object/background image intensity distribution, and an energy of a second-order binary Markov-Gibbs random field spatial model. At the third stage, residual segmentation errors and imperfection of image alignment are reduced by employing a Laplace-based registration refinement step that provides accurate pixel-on-pixel matches on all segmented frames to generate accurate parametric perfusion maps. Finally, physiology is characterized by pixel-by-pixel mapping of empirical indexes (peak signal intensity, time-to-peak, initial upslope, and the average signal change of the slowly varying agent delivery phase), based on contrast agent dynamics. The authors tested our framework on 24 perfusion data sets from 8 patients with ischemic damage who are undergoing a novel myoregeneration therapy. The performance of the processing steps of our framework is evaluated using both synthetic and in-vivo data. First, our registration methodology is evaluated using realistic synthetic phantoms and a distance-based error metric, and an improvement of registration is documented using the proposed similarity measure (P-value ≤10(-4)). Second, evaluation of our segmentation using the Dice similarity coefficient, documented an average of 0.910 ± 0.037 compared to two other segmentation methods that achieved average values of 0.862 ± 0.045 and 0.844 ± 0.047. Also, the receiver operating characteristic (ROC) analysis of our multifeature segmentation yielded an area under the ROC curve of 0.92, while segmentation based intensity alone showed low performance (an area of 0.69). Moreover, our framework indicated the ability, using empirical perfusion indexes, to reveal regional perfusion improvements with therapy and transmural perfusion differences across the myocardial wall. By quantitative and visual assessment, our framework documented the ability to characterize regional and transmural perfusion, thereby it augmenting the ability to assess follow-up treatment for patients undergoing myoregeneration therapy. This is afforded by our framework being able to handle both global and local deformations of the heart, segment accurately the myocardial wall, and provide accurate pixel-on-pixel matches of registered perfusion images.
IEEE Journal of Biomedical and Health Informatics, 2015
In this paper, we propose a novel framework for the automated extraction of the brain from T1-wei... more In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model (a two-level Markov-Gibbs random field (MGRF)) that serves to learn the visual appearance of the brain texture, and a geometric model (the brain iso-surfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: (i) Following bias correction of the brain, a new 3D MGRF having a 26- pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3D edges between different brain tissues; (ii) The non-brain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested iso-surfaces using a fast marching level set method; (iii) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the iso-surfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in-vivo data using 300 infant 3D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 data sets based on three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used brain extraction tools: BET, BET2, brain surface extractor (BSE), and infant brain extraction and analysis toolbox (iBEAT). Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.
Purpose: To present a review of most commonly used techniques to analyze dynamic contrastenhanced... more Purpose: To present a review of most commonly used techniques to analyze dynamic contrastenhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. Methods: DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal-or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. Results: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. Conclusions: Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion. C Khalifa et al.: Models and methods for analyzing DCE-MRI 124301-2 F. 1. Cross section DCE-MRI images of the heart, kidney, and prostate at different time instants before and after administering the CA into the blood stream.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011
Kidney segmentation is a key step in developing any noninvasive computer-aided diagnosis (CAD) sy... more Kidney segmentation is a key step in developing any noninvasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.
ABSTRACT We propose a novel, fast, robust, bi-directional coupled parametric deformable model to ... more ABSTRACT We propose a novel, fast, robust, bi-directional coupled parametric deformable model to segment the left ventricle (LV) wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of LV wall to track the evolution of the parametric deformable models control points. To accurately estimate the marginal density of each deformable model control point, the empirical marginal grey level distributions (first-order appearance) inside and outside the boundary of the deformable model are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The second order visual appearance of the LV wall is accurately modeled with a new rotationally invariant second-order Markov-Gibbs random field (MGRF). We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926+/-0.022 and AD value of 2.16+/-0.60 compared to two other level set methods that achieve 0.904+/-0.033 and 0.885+/-0.02 for DSC; and 2.86+/-1.35 and 5.72+/-4.70 for AD, respectively.
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013
ABSTRACT We propose a shape-based approach for the segmentation of the left ventricle (LV) wall o... more ABSTRACT We propose a shape-based approach for the segmentation of the left ventricle (LV) wall on cardiac first-pass magnetic resonance imaging (FP-MRI) using level sets. To reduce the variabilities of the LV wall in FP-MRI, it is first imperative to co-align the time series images to account for the global and local motions of the heart. Therefore, we developed a two-step registration methodology that includes an affine-based registration followed by a local B-splines based alignment to maximize a similarity function that accounts for the first- and second-order normalized mutual information (NMI). Additionally, myocardial signal intensity varies with the agent transit, which makes it difficult to control the level set evolution using image intensities alone. Thus, we constrained the level set evolution using three features: a weighted probabilistic shape prior, the first-order pixel-wise image intensities, and a second-order Markov-Gibbs random field (MGRF) spatial interaction model. We tested our approach on 24 data sets in 8 infarction patients using the Dice similarity coefficient (DSC), comparing our approach to other shape-based segmentation approaches. We also tested the performance of our segmentation approach using the receiver operating characteristics (ROC). Our approach achieved a mean DSC value of 0.910±0.037 compared to other shape-based methods that achieved 0.862±0.045 and 0.844±0.047. Finally, the ROC analysis for our segmentation method showed the best performance, with area under the ROC curve of 0.92, while that for intensity showed the worst performance, with area under the ROC curve of 0.69.
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
Abstract Prostate segmentation is an essential step in developing any non-invasive Computer-Assis... more Abstract Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, we propose, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement Magnetic Resonance Images (DCE-MRI). The framework is based on a maximum aposteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors:(i) 1 st-order ...
Enhancing the total system performance along with supporting a large number of users is the chall... more Enhancing the total system performance along with supporting a large number of users is the challenge of cellular service providers. Code division multiple access (CDMA) system capacity as an interference limited capacity performance is mainly affected by the out of-cell interference. While the in-cell interference is controlled within each cell using power control algorithms, the out of -cell interference is not, and may impact network coverage and the total user capacity. One approach used to enhance users' quality of service, increase the system capacity, and widen the system coverage is to use cells of different radii, hierarchically organized in overlapping layers, this is known as multitier system. In this paper the system capacity in the uplink direction of a certain two-tier CDMA system consisting of two cells operating at the same frequency band is examined. A fast/slow mobility profile is used to study the effect of the users' mobility on the call blocking and call dropping probability for a Signal-to-Interference Ratio (SIR)-based uplink admission control scheme. Two schemes for uplink capacity calculation are utilized. For the first scheme, to have a non negative solution of the received powers of current active users, i.e., feasible solution, a randomly selected user(s) is(are) dropped. For the second scheme, the user with highest amount of interference is dropped regardless of the cell membership. Computer simulation results show that, for the second scheme, the system capacity increased roughly by about 8 % over the first scheme and the dropping probability is 50 % less than that of the first scheme. The dominant parameters affecting the system performance are the users' velocity, new users' arrival rate, and the distance between the two cells.
To discriminate more accurately between dyslexic and normal brains, we detect the brain cortex va... more To discriminate more accurately between dyslexic and normal brains, we detect the brain cortex variability through a spherical harmonic analysis that represents a 3D surface supported by the unit sphere, having a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D brain cortex segmentation, with a deformable 3D boundary, controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the brain cortex surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface, and (v) determining the number of the SHs to delineate the brain cortex. We describe the brain shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into the normal and dyslexic brains. Initial experiments suggest that our shape index is a promising supplement to the current dyslexia diagnostic techniques.
An alternative method for diagnosing malignant lung nodules by their shape rather than convention... more An alternative method for diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis, which represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface, and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification to distinguish malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 93.6% correct classification (for the 95% confidence interval), showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.
A new speed function to guide evolution of a level-set based active contour is proposed for segme... more A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The evolving contour interior is modeled by a 2nd-order translation and rotation invariant Markov-Gibbs random field of object / background labels with analytically estimated potentials. Experiments to segment the inner cavity of heart cine images confirm robustness and accuracy of the proposed approach.
Autism is a complex developmental disability that typically appears during the first 3 years of l... more Autism is a complex developmental disability that typically appears during the first 3 years of life, and is the result of a neurological disorder that affects the normal functioning of the brain, impacting development in the areas of social interaction and communication skills. Early detection allows for treatments to be attempted, thus minimizing the impact of the autism on the individual. Given currently available diagnostic instruments, autism and other pervasive developmental disorders are difficult to detect in very young children. While shape based statistical analysis methods for autism are still in their early stages, current results show positive outlooks on the ability to detect differences between autistic and non-autistic patients. A framework is proposed that is capable of taking two-dimensional images from a standard medical scanner, and be able to construct a three-dimensional representation of the object and examine it through combination of its weighted linear spherical harmonics. The desired outcome is that a distinction can be made between the analysis of autistic and non-autistic brain data. The reconstruction analysis process involves linearly combining spherical harmonics of the corresponding mesh. It was expected that due to the complexity of the brain of an autistic subject it would require more iterations of reconstruction to reach convergence of the same error level as compared to the brain of a non-autistic subject. This was confirmed by the data. Using this method of analyzing the data a significant difference can be demonstrated between groups of examined subjects. The research clearly demonstrates that the non-autistic subjects’ data converges both faster and with a lower rate of error level than the data taken from a person with autism.
A new speed function to guide evolution of a level-set based active contour is proposed for segme... more A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1 ×Ø -order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2 Ò -order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The evolving contour interior is modeled by a ¾ Ò -order translation and rotation invariant Markov-Gibbs random field of object / background labels with analytically estimated potentials. Experiments with kidney CT images confirm robustness and accuracy of the proposed approach.
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagn... more Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. The framework was evaluated on in vivo prostate DW-MRI with available manual expert segmentation. The performance evaluation of the proposed segmentation approach, based on voxel-based and distance-based metrics between manually drawn and automatically segmented contours, confirmed the robustness and accuracy of the proposed segmentation approach.
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagn... more Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors: (i) 1st-order visual appearance descriptors of the Diffusion-MRI, (ii) a 3D spatially rotation-variant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate Diffusion-MRI data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate data. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs Random Field (MGRF) of object/background labels with analytically estimated potentials. Experiments with real in vivo prostate Diffusion-MRI confirm the robustness and accuracy of the proposed approach.
Our long-term research goal is to develop a fully automated, image-based diagnostic system for ea... more Our long-term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring the detected lung nodules includes 3-D LDCT data registration, which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. This approach has been validated on elastic lung phantoms constructed using state-of-the-art microfluidics technology. The elastic lung phantoms are fabricated from a flexible transparent polymer, i.e., polydimethylsiloxane (PDMS). These Phantoms mimic the contractions and expansions of the lung and nodules seen during normal breathing. Experiments confirm the high accuracy of the proposed approach for measuring the growth rate of the detected lung nodules.
Abstract To discriminate more accurately between dyslexic and normal brains, we detect the brain ... more Abstract To discriminate more accurately between dyslexic and normal brains, we detect the brain cortex variability through a spherical harmonic analysis that represents a 3D surface supported by the unit sphere, having a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps:(i) 3D brain cortex segmentation, with a deformable 3D boundary, controlled by two probabilistic visual appearance models (the learned prior and the estimated current ...
A promising approach for the automatic classification of normal and acute rejection transplants f... more A promising approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is proposed. The proposed approach consists of three main steps. The first step segments the kidney from the surrounding abdominal tissues by a level-set based deformable model with a speed function that accounts for a learned spatially variant statistical shape prior, 1 st -order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2 nd -order homogeneity descriptor. In the second step, to handle local object deformations due to kidney motion caused by patient breathing, we proposed a new nonrigid approach to align the object by solving Laplace's equation between closed equispaced contours (iso-contours) of the reference and target objects. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the segmented kidneys and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction.
Almost all computer vision applications, from remote sensing and cartography to medical imaging a... more Almost all computer vision applications, from remote sensing and cartography to medical imaging and biometrics, use image registration or alignment techniques that establish spatial correspondence (one-to-one mapping) between two or more images. These images depict either one planar (2-D) or volumetric (3-D) scene or several such scenes and can be taken at different times, from various viewpoints, and/or by multiple sensors. In medical image processing and analysis, the image registration is instrumental for clinical diagnosis and therapy planning, e.g., to follow disease progression and/or response to treatment, or integrate information from different sources/modalities to form more detailed descriptions of anatomical objects-of-interest. The unified registration goal – aligning a 2-D or 3-D target (sensed) image with a reference image – is reached by specifying a mathematical model of image transformations for and determining model parameters of the desired alignment. Frequently, the parameters provide an optimum of a goal function supported by the parameter space, so that the registration reduces to a certain optimization problem. This chapter overviews the 2-D and the 3-D medical image registration with special reference to the state-of-the-art robust techniques proposed for the last decade and discusses their advantages, drawbacks, and practical implementations.
Noncontrast agent cine Cardiac Magnetic Resonance (CMR) acquisitions using gated images taken thr... more Noncontrast agent cine Cardiac Magnetic Resonance (CMR) acquisitions using gated images taken through the cardiac cycle to characterize function are a well-established part of a comprehensive CMR exam. Although these methods have long been explored, in practice, efficient parameter determination and effective displays remain an area that could benefit from continued improvement to encourage their routine use. In this chapter, we propose a novel and automated framework to (1) segment the inner and outer myocardial borders (contours) from the surrounding tissues in cine CMR images based on using a new stochastic speed function to control the evolution of a geometric (level set) deformable model, and (2) to estimate local wall thickness and thickening function index based on solving Laplace equation in the segmented wall to find point-to-point correspondences between the inner and outer border of the segmented wall. Effects of segmentation errors are reduced and a continuity analysis of the wall thickening values is performed by iteratively computing the Maximum A Posteriori (MAP) estimate of a pairwise energy function of a generalized Gauss-Markov random field (GGMRF) probabilistic model. We also use cavity areas derived from the inner region to index global function parameters (e.g., ejection fraction). Experiments with in vivo CMR data collected from eight independent data sets confirm higher robustness and accuracy of the proposed framework over the existing ones.
This dissertation is dedicated to my mother. Words cannot express how much I wish that she was st... more This dissertation is dedicated to my mother. Words cannot express how much I wish that she was still with me to see the completion of this endeavor.
This dissertation is dedicated to my mother. Words cannot express how much I wish that she was st... more This dissertation is dedicated to my mother. Words cannot express how much I wish that she was still with me to see the completion of this endeavor.
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Papers by Fahmi Khalifa