Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal... more Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal with the difficulties associated with outlier detection across various domains and data characteristics, ensembles of outlier detectors have recently been employed to improve the performance of individual outlier detectors. In this paper, we follow an ensemble outlier detection approach in which good outlier detectors are selected through an enhanced clustering-based dynamic selection (CBDS) method. In this method, a bisecting K-means clustering algorithm is employed to partition the input data into clusters where every cluster defines a local region of competence. Among the initial pool of detectors, the outputs of the detectors with the most competent local performance were combined through four possible schemes to produce the final OD results. Experimental evaluation and comparison of our method were carried out against four variants of locally selective combination in parallel (LSCP) outlier ensembles. The CBDS-based schemes compare well with the LSCP-based ones on 16 public benchmark datasets and incur considerably lower computational costs. The CBDS method consistently achieved superior average scores of the area under the curve (AUC) of the receiver operating characteristic (ROC), and particularly outperformed the LSCP method on nine of the 16 datasets in terms of the AUC score. In addition, while the CBDS and LSCP methods have similar computational costs on small datasets, the CBDS method achieves significant time savings compared with the LSCP method on large datasets. INDEX TERMS Bisecting K-means, dynamic detector selection, outlier detection, outlier ensemble.
Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical funct... more Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were ...
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheime... more Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.
Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thi... more Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time seri...
Biomedical Engineering: Applications, Basis and Communications, 2022
Microscopic digital image processing paves the way for study and evaluation of blastomere identif... more Microscopic digital image processing paves the way for study and evaluation of blastomere identification and localization as a preprocessing step for the embryos selection for the In VitroFertilization (IVF) transfer. Computer vision aims at developing automated image system to localize and grade blastomeres before injection. In this paper, we propose a clustering-based system that supports the localization and counting of blastomeres. The dataset, employed in this study, is formed of 50 Images collected at Assisted Reproduction Technology (ART) Unit, International Islamic Center for Population Studies and Research, Al-Azhar University, Egypt. The proposed system is formed of 2 modules named preprocessing and segmentation modules, where different algorithms were investigated for each module. The preprocessing module includes Image denoising and enhancement tasks. Whereas the edge enhancement investigates the performance of Ostu’s thresholding, Canny and Sobel edge detection techniqu...
Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early s... more Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of...
Content-based image retrieval system for brain magnetic resonance imaging based on pseudo-zernike... more Content-based image retrieval system for brain magnetic resonance imaging based on pseudo-zernike coefficients combined with wavelet features
BACKGROUND Assessment of regional myocardial function at native pixel-level resolution can play a... more BACKGROUND Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps εp1 and εp2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain). METHODS For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms. RESULTS CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with εp1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and εp2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for εp1 and εp2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively. CONCLUSION CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.
Background and Purpose The pathology of juvenile myoclonic epilepsy (JME) is related to thalamoco... more Background and Purpose The pathology of juvenile myoclonic epilepsy (JME) is related to thalamocortical dysfunction, and the putamen is closely related to the thalamocortical circuit. The purpose of this study is to evaluate the microstructure of the thalamo-cortico-striatal circuit in JME patients using diffusion tensor imaging (DTI). Materials and Methods This case–control study was conducted on 13 patients with JME (10 females and 3 males with a mean age of 23.38 ± 4.75 years) and 9 age- and sex-matched volunteers as control subjects. All subjects underwent DTI and conventional magnetic resonance imaging. The data were then processed via denoising, registration, and segmentation of the internal capsule and right and left putamen using the FMRIB Software Library (FSL). The FSL was also used to calculate the different anisotropy measures: the fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (LD), and radial diffusivity (RD). Results The patients with JME ...
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2016
Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentat... more Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles’ real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next fr...
2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017
In this study, we investigate three class skin lesion classification problem of a low quality and... more In this study, we investigate three class skin lesion classification problem of a low quality and small size dataset using transfer learning using AlexNet deep Convolutional Neural Network (CNN). Our approach involves modifying the pre-trained AlexNet model; through replacing the decision layer to be compatible with our three class problem. In addition, we propose adding two dropout layers to overcome the over fitting problem. The fine tuning process of the complete network, based on stochastic gradient descent, is performed using skin lesion dataset. Furthermore, we investigated augmenting the original dataset through three flipping directions and sixteen rotation angles processes using a new methodology. The proposed algorithm has been compared with a hand crafted features, based on Local Binary Pattern (LBP) representation followed by Support Vector Machine (SVM) classifier. Increasing the dataset size has dramatically boosted the performance of classifiers achieving accuracy of 98.67% for the modified AlexNet compared to 96.8% using the LBP based system.
2016 8th Cairo International Biomedical Engineering Conference (CIBEC), 2016
Restoration of upper limb movements for stroke recovery patients is an essential keystone in reha... more Restoration of upper limb movements for stroke recovery patients is an essential keystone in rehabilitative practices. The functional electrical stimulation (FES) systems had proved its success in restoring the movement in order to increase the ability to perform the daily living activities. In this study, we propose a Brain Computer Interface (BCI) system that controls the FES. The system is formed of a data acquisition system using ANT headset, signal preprocessing followed by a feature extraction module, and a classification module for the binary movements of the arm. The performance of both the online and offline learning has been studied for different combinations of features and classifiers. The wavelet and Fourier transform based features combined with the Linear Discriminant Analysis (LDA) for the classification task has proved its superiority in performance to reach 91.43% and 0.8257 for accuracy and mutual information (MI) respectively. Transcutaneous electrical stimulation (TENS) was selected for the FES module since it was optimum for our application.
2016 IEEE International Conference on Image Processing (ICIP), 2016
Analysis of retinal vessels in fundus images provides a valuable tool for characterizing many ret... more Analysis of retinal vessels in fundus images provides a valuable tool for characterizing many retinal and systemic diseases. Accurate automatic segmentation of these vessels is usually required as an essential analysis step. In this work, we propose a new formulation of deep Convolutional Neural Networks that allows simple and accurate segmentation of the retinal vessels. A major modification in this work is to reduce the intra-class variance by formulating the problem as a Three-class problem that differentiates: large vessels, small vessels, and background areas. In addition, different sizes of the convolutional kernels have been studied and it was found that a combination of kernels with different sizes achieve the best sensitivity and specificity. The proposed method was tested using DRIVE dataset and it showed superior performance compared to several other state of the art methods. The segmentation sensitivity, specificity and accuracy were found to be 83.97%, 95.62% and 94.56% respectively.
Journal of Medical Imaging and Health Informatics, 2012
A method for magnetic resonance image denoising based on wavelet domain bilateral filtering (WDBF... more A method for magnetic resonance image denoising based on wavelet domain bilateral filtering (WDBF) is proposed. The main problem in bilateral filtering based methods is that the choice of filtration parameters has a trade-off between preserving edges and noise removal. In this work, a solution that would allow different components of the image to be filtered using different parameters is presented. The bilateral filtering is applied in a customized manner to different wavelet subbands and followed by subband mixing to form the final image. The proposed method is implemented to filter magnetic resonance images and verified both qualitatively and quantitatively. Verification of the new method was carried out on synthetic as well as real data sets. Qualitative and quantitative comparisons with present techniques indicate that the proposed method produces superior denoising results and suggesting potential for clinical application to boost the signal-to-noise ratio of low magnetic field scanners.
2014 Cairo International Biomedical Engineering Conference (CIBEC), 2014
Cardiovascular diseases remain the biggest cause of death worldwide. Early detection is the most ... more Cardiovascular diseases remain the biggest cause of death worldwide. Early detection is the most important step to solve this problem. Cardiac Magnetic Resonance Imaging (CMR) has shown its capability of imaging the heart and evaluation of cardiac function without exposure to ionizing radiation. In this paper, we aim to perform automatic heart localization in MRI short axis view using a simple and effective way. The proposed algorithm can be described through the following two steps: (a) preprocessing step to denoise the images using Gaussian filter, and (b) localization step based on shape recognition technique. The algorithm, implemented using MATLAB, was developed and tested using two datasets. First one consists of 33 subjects normal and abnormal for a total of 7980 2D images and the second one consists of 50 2D images related to normal subject. The results here are evaluated through the classification into three types: totally localized case (complete heart falls inside the selected ROI window), partially localized case (small part, less than 10%, of LV or RV falls outside the selected ROI window), and not localized case (more than 10% of the hearts falls outside the selected ROI window). It gives result with 91.4% accuracy for totally localized case, 8.1% for partially localized case, and 0.5% for not localized case.
Abstract Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging mod... more Abstract Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging modality to study the changes of functional brain networks in schizophrenic patients. Several representations have been proposed to capture the essential features of these networks. In particular, graph-theoretic representations can be effectively used to discriminate healthy subjects from schizophrenic patients. In this paper, we propose a machine-learning system based on a graph-theoretic approach to investigate and differentiate the brain network alterations. The fMRI data samples are first preprocessed to reduce noise and normalize the images. The automated anatomical labeling (AAL) atlas is then used to parcellate the brain into 90 regions and construct a region connectivity matrix. A weighted undirected graph is hence constructed and graph measures are computed for each subject. These graph measures include betweenness centrality, characteristic path length, degree, clustering coefficient, local efficiency, global efficiency, participation coefficient and small-worldness. After that, feature selection algorithms are used to choose the most discriminant features. Finally, a SVM classifier is trained and tested on discriminant graph features. Experiments were performed on a large Rs-fMRI dataset formed of 70 schizophrenic patients and 70 healthy subjects. The performance was evaluated using nested-loop 10-fold cross-validation. The best detection results were found using the feature selection methods of Welch's t-test (82.85%), l0-norm (91.43%), and feature selection via concave minimization (FSV) (95.00%). Our results outperform those of recent state-of-the-art graph-theoretic methods.
INTRODUCTION Neurocognitive impairment represents one of the most common comorbidities occurring ... more INTRODUCTION Neurocognitive impairment represents one of the most common comorbidities occurring in children with idiopathic epilepsy. Diagnosis of the idiopathic form of epilepsy requires the absence of any macrostructural abnormality in the conventional MRI. Though changes can be seen at the microstructural level imaged using advanced techniques such as the Diffusion Tensor Imaging (DTI). AIM OF THE WORK The aim of this work is to study the correlation between the microstructural white matter DTI findings, the electroencephalographic changes and the cognitive dysfunction in children with active idiopathic epilepsy. METHODS A comparative cross-sectional study, included 60 children with epilepsy based on the Stanford-Binet 5th Edition Scores was conducted. Patients were equally assigned to normal cognitive function or cognitive dysfunction groups. The history of the epileptic condition was gathered via personal interviews. All patients underwent brain Electroencephalography (EEG) and DTI, which was analyzed using FSL. RESULTS The Fractional Anisotropy (FA) was significantly higher whereas the Mean Diffusivity (MD) was significantly lower in the normal cognitive function group than in the cognitive dysfunction group. This altered microstructure was related to the degree of the cognitive performance of the studied children with epilepsy. The microstructural alterations of the neural fibers in children with epilepsy and cognitive dysfunction were significantly related to the younger age of onset of epilepsy, the poor control of the clinical seizures, and the use of multiple antiepileptic medications. CONCLUSION Children with epilepsy and normal cognitive functions differ in white matter integrity, measured using DTI, compared with children with cognitive dysfunction. These changes have important cognitive consequences.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Recently, computerized arrhythmia classification tools have been intensively used to aid physicia... more Recently, computerized arrhythmia classification tools have been intensively used to aid physicians to recognize different irregular heartbeats. In this paper, we introduce arrhythmia CAD system exploiting cyclostationary signal analysis through estimation of the spectral correlation function for 5 different beat types. Two experiments were performed. Raw spectral correlation data were used as features in the first experiment while the other experiment which dealt with the spectral correlation coefficients as image included extraction of wavelet and shape features followed by fisher score for dimensionality reduction. As for the classification task, Support Vector Machine (SVM) with linear kernel was used for both experiments. The experimental results showed that both proposed approaches are superior compared to several state of the art methods. This approach achieved sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 99.20%, 99.70%, 98.60%, 99.90% and 97.60% respectively.
Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal... more Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal with the difficulties associated with outlier detection across various domains and data characteristics, ensembles of outlier detectors have recently been employed to improve the performance of individual outlier detectors. In this paper, we follow an ensemble outlier detection approach in which good outlier detectors are selected through an enhanced clustering-based dynamic selection (CBDS) method. In this method, a bisecting K-means clustering algorithm is employed to partition the input data into clusters where every cluster defines a local region of competence. Among the initial pool of detectors, the outputs of the detectors with the most competent local performance were combined through four possible schemes to produce the final OD results. Experimental evaluation and comparison of our method were carried out against four variants of locally selective combination in parallel (LSCP) outlier ensembles. The CBDS-based schemes compare well with the LSCP-based ones on 16 public benchmark datasets and incur considerably lower computational costs. The CBDS method consistently achieved superior average scores of the area under the curve (AUC) of the receiver operating characteristic (ROC), and particularly outperformed the LSCP method on nine of the 16 datasets in terms of the AUC score. In addition, while the CBDS and LSCP methods have similar computational costs on small datasets, the CBDS method achieves significant time savings compared with the LSCP method on large datasets. INDEX TERMS Bisecting K-means, dynamic detector selection, outlier detection, outlier ensemble.
Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical funct... more Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were ...
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheime... more Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.
Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thi... more Alzheimer’s disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features’ strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time seri...
Biomedical Engineering: Applications, Basis and Communications, 2022
Microscopic digital image processing paves the way for study and evaluation of blastomere identif... more Microscopic digital image processing paves the way for study and evaluation of blastomere identification and localization as a preprocessing step for the embryos selection for the In VitroFertilization (IVF) transfer. Computer vision aims at developing automated image system to localize and grade blastomeres before injection. In this paper, we propose a clustering-based system that supports the localization and counting of blastomeres. The dataset, employed in this study, is formed of 50 Images collected at Assisted Reproduction Technology (ART) Unit, International Islamic Center for Population Studies and Research, Al-Azhar University, Egypt. The proposed system is formed of 2 modules named preprocessing and segmentation modules, where different algorithms were investigated for each module. The preprocessing module includes Image denoising and enhancement tasks. Whereas the edge enhancement investigates the performance of Ostu’s thresholding, Canny and Sobel edge detection techniqu...
Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early s... more Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of...
Content-based image retrieval system for brain magnetic resonance imaging based on pseudo-zernike... more Content-based image retrieval system for brain magnetic resonance imaging based on pseudo-zernike coefficients combined with wavelet features
BACKGROUND Assessment of regional myocardial function at native pixel-level resolution can play a... more BACKGROUND Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps εp1 and εp2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain). METHODS For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms. RESULTS CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with εp1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and εp2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for εp1 and εp2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively. CONCLUSION CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.
Background and Purpose The pathology of juvenile myoclonic epilepsy (JME) is related to thalamoco... more Background and Purpose The pathology of juvenile myoclonic epilepsy (JME) is related to thalamocortical dysfunction, and the putamen is closely related to the thalamocortical circuit. The purpose of this study is to evaluate the microstructure of the thalamo-cortico-striatal circuit in JME patients using diffusion tensor imaging (DTI). Materials and Methods This case–control study was conducted on 13 patients with JME (10 females and 3 males with a mean age of 23.38 ± 4.75 years) and 9 age- and sex-matched volunteers as control subjects. All subjects underwent DTI and conventional magnetic resonance imaging. The data were then processed via denoising, registration, and segmentation of the internal capsule and right and left putamen using the FMRIB Software Library (FSL). The FSL was also used to calculate the different anisotropy measures: the fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (LD), and radial diffusivity (RD). Results The patients with JME ...
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2016
Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentat... more Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles’ real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next fr...
2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017
In this study, we investigate three class skin lesion classification problem of a low quality and... more In this study, we investigate three class skin lesion classification problem of a low quality and small size dataset using transfer learning using AlexNet deep Convolutional Neural Network (CNN). Our approach involves modifying the pre-trained AlexNet model; through replacing the decision layer to be compatible with our three class problem. In addition, we propose adding two dropout layers to overcome the over fitting problem. The fine tuning process of the complete network, based on stochastic gradient descent, is performed using skin lesion dataset. Furthermore, we investigated augmenting the original dataset through three flipping directions and sixteen rotation angles processes using a new methodology. The proposed algorithm has been compared with a hand crafted features, based on Local Binary Pattern (LBP) representation followed by Support Vector Machine (SVM) classifier. Increasing the dataset size has dramatically boosted the performance of classifiers achieving accuracy of 98.67% for the modified AlexNet compared to 96.8% using the LBP based system.
2016 8th Cairo International Biomedical Engineering Conference (CIBEC), 2016
Restoration of upper limb movements for stroke recovery patients is an essential keystone in reha... more Restoration of upper limb movements for stroke recovery patients is an essential keystone in rehabilitative practices. The functional electrical stimulation (FES) systems had proved its success in restoring the movement in order to increase the ability to perform the daily living activities. In this study, we propose a Brain Computer Interface (BCI) system that controls the FES. The system is formed of a data acquisition system using ANT headset, signal preprocessing followed by a feature extraction module, and a classification module for the binary movements of the arm. The performance of both the online and offline learning has been studied for different combinations of features and classifiers. The wavelet and Fourier transform based features combined with the Linear Discriminant Analysis (LDA) for the classification task has proved its superiority in performance to reach 91.43% and 0.8257 for accuracy and mutual information (MI) respectively. Transcutaneous electrical stimulation (TENS) was selected for the FES module since it was optimum for our application.
2016 IEEE International Conference on Image Processing (ICIP), 2016
Analysis of retinal vessels in fundus images provides a valuable tool for characterizing many ret... more Analysis of retinal vessels in fundus images provides a valuable tool for characterizing many retinal and systemic diseases. Accurate automatic segmentation of these vessels is usually required as an essential analysis step. In this work, we propose a new formulation of deep Convolutional Neural Networks that allows simple and accurate segmentation of the retinal vessels. A major modification in this work is to reduce the intra-class variance by formulating the problem as a Three-class problem that differentiates: large vessels, small vessels, and background areas. In addition, different sizes of the convolutional kernels have been studied and it was found that a combination of kernels with different sizes achieve the best sensitivity and specificity. The proposed method was tested using DRIVE dataset and it showed superior performance compared to several other state of the art methods. The segmentation sensitivity, specificity and accuracy were found to be 83.97%, 95.62% and 94.56% respectively.
Journal of Medical Imaging and Health Informatics, 2012
A method for magnetic resonance image denoising based on wavelet domain bilateral filtering (WDBF... more A method for magnetic resonance image denoising based on wavelet domain bilateral filtering (WDBF) is proposed. The main problem in bilateral filtering based methods is that the choice of filtration parameters has a trade-off between preserving edges and noise removal. In this work, a solution that would allow different components of the image to be filtered using different parameters is presented. The bilateral filtering is applied in a customized manner to different wavelet subbands and followed by subband mixing to form the final image. The proposed method is implemented to filter magnetic resonance images and verified both qualitatively and quantitatively. Verification of the new method was carried out on synthetic as well as real data sets. Qualitative and quantitative comparisons with present techniques indicate that the proposed method produces superior denoising results and suggesting potential for clinical application to boost the signal-to-noise ratio of low magnetic field scanners.
2014 Cairo International Biomedical Engineering Conference (CIBEC), 2014
Cardiovascular diseases remain the biggest cause of death worldwide. Early detection is the most ... more Cardiovascular diseases remain the biggest cause of death worldwide. Early detection is the most important step to solve this problem. Cardiac Magnetic Resonance Imaging (CMR) has shown its capability of imaging the heart and evaluation of cardiac function without exposure to ionizing radiation. In this paper, we aim to perform automatic heart localization in MRI short axis view using a simple and effective way. The proposed algorithm can be described through the following two steps: (a) preprocessing step to denoise the images using Gaussian filter, and (b) localization step based on shape recognition technique. The algorithm, implemented using MATLAB, was developed and tested using two datasets. First one consists of 33 subjects normal and abnormal for a total of 7980 2D images and the second one consists of 50 2D images related to normal subject. The results here are evaluated through the classification into three types: totally localized case (complete heart falls inside the selected ROI window), partially localized case (small part, less than 10%, of LV or RV falls outside the selected ROI window), and not localized case (more than 10% of the hearts falls outside the selected ROI window). It gives result with 91.4% accuracy for totally localized case, 8.1% for partially localized case, and 0.5% for not localized case.
Abstract Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging mod... more Abstract Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging modality to study the changes of functional brain networks in schizophrenic patients. Several representations have been proposed to capture the essential features of these networks. In particular, graph-theoretic representations can be effectively used to discriminate healthy subjects from schizophrenic patients. In this paper, we propose a machine-learning system based on a graph-theoretic approach to investigate and differentiate the brain network alterations. The fMRI data samples are first preprocessed to reduce noise and normalize the images. The automated anatomical labeling (AAL) atlas is then used to parcellate the brain into 90 regions and construct a region connectivity matrix. A weighted undirected graph is hence constructed and graph measures are computed for each subject. These graph measures include betweenness centrality, characteristic path length, degree, clustering coefficient, local efficiency, global efficiency, participation coefficient and small-worldness. After that, feature selection algorithms are used to choose the most discriminant features. Finally, a SVM classifier is trained and tested on discriminant graph features. Experiments were performed on a large Rs-fMRI dataset formed of 70 schizophrenic patients and 70 healthy subjects. The performance was evaluated using nested-loop 10-fold cross-validation. The best detection results were found using the feature selection methods of Welch's t-test (82.85%), l0-norm (91.43%), and feature selection via concave minimization (FSV) (95.00%). Our results outperform those of recent state-of-the-art graph-theoretic methods.
INTRODUCTION Neurocognitive impairment represents one of the most common comorbidities occurring ... more INTRODUCTION Neurocognitive impairment represents one of the most common comorbidities occurring in children with idiopathic epilepsy. Diagnosis of the idiopathic form of epilepsy requires the absence of any macrostructural abnormality in the conventional MRI. Though changes can be seen at the microstructural level imaged using advanced techniques such as the Diffusion Tensor Imaging (DTI). AIM OF THE WORK The aim of this work is to study the correlation between the microstructural white matter DTI findings, the electroencephalographic changes and the cognitive dysfunction in children with active idiopathic epilepsy. METHODS A comparative cross-sectional study, included 60 children with epilepsy based on the Stanford-Binet 5th Edition Scores was conducted. Patients were equally assigned to normal cognitive function or cognitive dysfunction groups. The history of the epileptic condition was gathered via personal interviews. All patients underwent brain Electroencephalography (EEG) and DTI, which was analyzed using FSL. RESULTS The Fractional Anisotropy (FA) was significantly higher whereas the Mean Diffusivity (MD) was significantly lower in the normal cognitive function group than in the cognitive dysfunction group. This altered microstructure was related to the degree of the cognitive performance of the studied children with epilepsy. The microstructural alterations of the neural fibers in children with epilepsy and cognitive dysfunction were significantly related to the younger age of onset of epilepsy, the poor control of the clinical seizures, and the use of multiple antiepileptic medications. CONCLUSION Children with epilepsy and normal cognitive functions differ in white matter integrity, measured using DTI, compared with children with cognitive dysfunction. These changes have important cognitive consequences.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Recently, computerized arrhythmia classification tools have been intensively used to aid physicia... more Recently, computerized arrhythmia classification tools have been intensively used to aid physicians to recognize different irregular heartbeats. In this paper, we introduce arrhythmia CAD system exploiting cyclostationary signal analysis through estimation of the spectral correlation function for 5 different beat types. Two experiments were performed. Raw spectral correlation data were used as features in the first experiment while the other experiment which dealt with the spectral correlation coefficients as image included extraction of wavelet and shape features followed by fisher score for dimensionality reduction. As for the classification task, Support Vector Machine (SVM) with linear kernel was used for both experiments. The experimental results showed that both proposed approaches are superior compared to several state of the art methods. This approach achieved sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 99.20%, 99.70%, 98.60%, 99.90% and 97.60% respectively.
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Papers by Inas Yassine