Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an import... more Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an important preprocessing step in neuroimaging analysis. There are many tools developed to handle human fMRI images, which could involve manual steps for verifying results from brain segmentation that makes it time consuming and inefficient. In this study, we will use the segment anything model (SAM), a freely available neural network released by Meta[4], which has shown promising results in many generic segmentation applications. We will analyze the efficiency of SAM for neuroimaging brain segmentation by removing skull artifacts. The results of the experiments showed promising results that explore using automated segmentation algorithms for neuroimaging without the need to train on custom medical imaging dataset.
Journal of Neurology Research Review & Reports, Nov 29, 2023
Reproducibility is a key component of scientific research, and its significance has been increasi... more Reproducibility is a key component of scientific research, and its significance has been increasingly recognized in the field of Neuroscience. This paper explores the origin, need, and benefits of reproducibility in Neuroscience research, as well as the current landscape surrounding this practice, and further adds how boundaries of current reproducibility should be expanded to computing infrastructure. The reproducibility movement stems from concerns about the credibility and reliability of scientific findings in various disciplines, including Neuroscience. The need for reproducibility arises from the importance of building a robust knowledge base and ensuring the reliability of research findings. Reproducible studies enable independent verification, reduce the dissemination of false or misleading results, and foster trust and integrity within the scientific community. Collaborative efforts and knowledge sharing are facilitated, leading to accelerated scientific progress and the translation of research into practical applications. On the data front, we have platforms such as openneuro for open data sharing, on the analysis front we have containerized processing pipelines published in public repos which are reusable. There are also platforms such as openneuro, NeuroCAAS, brainlife etc which caters to the need for a computing platform. However, along with benefits these platforms have limitations as only set types of processing pipelines can be run on the data. Also, in the world of data integrity and governance, it may not be far in the future that some countries may require to process the data within the boundaries limiting the usage of the platform. To introduce customized, scalable neuroscience research, alongside open data, containerized analysis open to all, we need a way to deploy cloud infrastructure required for the analysis with templates. These templates are a blueprint of infrastructure required for reproducible research/analysis in a form of code. This will empower anyone to deploy computational infrastructure on cloud and use data processing pipeline on their own infrastructure of their choice and magnitude. Just as docker files are created for any analysis software developed, an IAC template accompanied with any published analysis pipeline, will enable users to deploy infrastructure on cloud required to carry out analysis on their data.
Convolutional Neural Networks (CNNs) have greatly influenced the field of Embedded Vision and Edg... more Convolutional Neural Networks (CNNs) have greatly influenced the field of Embedded Vision and Edge Artificial Intelligence (AI), enabling powerful machine learning capabilities on resource-constrained devices. This article explores the relationship between CNN compute requirements and memory bandwidth in the context of Edge AI. We delve into the historical progression of CNN architectures, from the early pioneering models to the current state-of-the-art designs, highlighting the advancements in compute-intensive operations. We examine the impact of increasing model complexity on both computational requirements and memory access patterns. The paper presents a comparison analysis of the evolving trade-off between compute demands and memory bandwidth requirements in CNNs. This analysis provides insights into designing efficient architectures and potential hardware accelerators in enhancing CNN performance on edge devices.
Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implement... more Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provide accurate estimates of core body temperature and are preferred sites for fever screening. Therefore, rapid, automated identification of the canthi regions within facial IR images may greatly facilitate rapid fever screening of asymptomatic travelers. However, it is more difficult to accurately identify the canthi regions from IR images than from visible images that are rich with exploitable features. In this study, we developed and evaluated techniques for multi-modality image registration (MMIR) of simultaneously captured visible and IR facial images for fever screening. We used free form deformation (FFD) models based on edge maps to improve registration accuracy after an affine transformation. Two widely used FFD models in medical image registration based on the Demons and cubic B-spline algorithms were qualitatively compared. The results showed that the Demons algorithm outperformed the cubic B-spline algorithm, likely due to overfitting of outliers by the latter method. The quantitative measure of registration accuracy, obtained through selected control point correspondence, was within 2.8 ± 1.2 mm, which enables accurate and automatic localization of canthi regions in the IR images for temperature measurement.
Title of thesis: IR THERMOGRAPHY AND IMAGE PHOTOPLETHYSMOGRAPHY : NON-CONTACT MEDICAL COUNTER MEA... more Title of thesis: IR THERMOGRAPHY AND IMAGE PHOTOPLETHYSMOGRAPHY : NON-CONTACT MEDICAL COUNTER MEASURE FOR INFECTION SCREENING Yedukondala Narendra Dwith Chenna, Master of Science, 2017 Thesis directed by: Professor Dr. Rama Chellappa Department of Electrical and Computer Engineering Screening based on non-contact infrared thermometers (NCITs) and Infrared Thermography (IRTG) shows promising results for mass fever screening. IRTGs were found to be powerful, quick and non-invasive methods to detect elevated temperatures. In the case of temperature measurement using IRTGs, regions medically adjacent to inner canthi are preferred sites for fever screening (IEC 80601-2-59:2008), which show good stability and correlation with body temperature. However, detection of canthi in thermal images is challenging due to the absence of features unlike visible images, which have sharp features that can be used for eye corner detection. We use registration of thermal images with visible light images ...
This paper proposes an efficient VLSI architecture for the implementation of variable block size ... more This paper proposes an efficient VLSI architecture for the implementation of variable block size motion estimation (VBSME). To improve the performance video compression the Variable Block Size Motion Estimation (VBSME) is the critical path. Variable Block Size Motion Estimation feature has been introduced in to the H.264/AVC. This feature induces significant complexities into the design of the H.264/AVC video codec. This paper we compare the existing architectures for VBSME. An efficient architecture to improve the performance of Spiral Search for Variable Size Motion Estimation in H.264/AVC is proposed. Among various architectures available for VBSME spiral search provides hardware friendly data flow with efficient utilization of resources. The proposed implementation is verified using the MATLAB on foreman, coastguard and train sequences. The proposed Adaptive thresholding technique reduces the average number of computations significantly with negligible effect on the video qualit...
International Journal of Computer Applications, 2013
In this paper, a novel parallelized implementation of rotation invariant texture classification u... more In this paper, a novel parallelized implementation of rotation invariant texture classification using Heterogeneous Computing Platforms like CPU and Graphics Processing Unit (GPU) is proposed. A complete modeling of the LBP operator as well as its improvised versions of Complete Local Binary Patterns (CLBP) and Multi-scale Local Binary Patterns (MLBP) has been developed on a CPU and GPU based Heterogeneous computing platforms using OpenCL. The tests using these feature descriptors of Local Binary Pattern (LBP) algorithms and their parallelized implementation using OpenCL were also performed. Significant Improvement in computation speed is achieved over traditional CPU-based algorithms. To test the accuracy of the GPU implemented algorithms a set of textures were classified using selected LBP, CLBP and MLBP descriptors. Classification was performed by applying these descriptors to several unique texture classes at various spatial resolutions and rotations. The primary focus of this paper is to provide an overview of these algorithms, demonstrate observed performance gains and to verify the validity of using these descriptors for texture analysis on a CPU and GPU based Heterogeneous Platform.
Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tum... more Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. In this paper, we propose an efficient wavelet based algorithm for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. Hence this algorithm effectively uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the efficiency of tumor detection. We also evaluate the effectiveness of proposed algorithm on varying the wavelet fusion parameters like number of decompositions, type of wavelet used for the decomposition. The experimental results of the simulation on MRI and CT images show the performance efficiency of the proposed approach.
2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2012
Face recognition is the biometric identification of human's face and matching the image against a... more Face recognition is the biometric identification of human's face and matching the image against a library of known faces. The algorithm used to simulate the above is Eigen faces algorithm. The software which is been proposed to implement is OpenCL. OpenCL(Open Computing Language) is an open standard for general purpose parallel programming across CPUs, GPUs and other processors, is portable and provides efficient access to the power of these heterogeneous processing platforms. The OpenCL API has functions to identify devices, compile programs, send and receive information and run OpenCL programs on the chosen device. The proposed system is been simulated in MSVS (Microsoft visual studio 2010) using AMD APP SDK (Advanced Micro Devices Accelerated Parallel Processing Software Development Kit).
International Journal of Image, Graphics and Signal Processing, 2013
Brain tumor, is one of the major causes for the increase in mortality among children and adults. ... more Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.
This paper proposes an efficient VLSI architecture for the implementation of variable block size ... more This paper proposes an efficient VLSI architecture for the implementation of variable block size motion estimation (VBSME). To improve the performance video compression the Variable Block Size Motion Estimation (VBSME) is the critical path. Variable Block Size Motion Estimation feature has been introduced in to the H.264/AVC. This feature induces significant complexities into the design of the H.264/AVC video codec. This paper we compare the existing architectures for VBSME. An efficient architecture to improve the performance of Spiral Search for Variable Size Motion Estimation in H.264/AVC is proposed. Among various architectures available for VBSME spiral search provides hardware friendly data flow with efficient utilization of resources. The proposed implementation is verified using the MATLAB on foreman, coastguard and train sequences. The proposed Adaptive thresholding technique reduces the average number of computations significantly with negligible effect on the video quality. The results are verified using hardware implementation on Xilinx Virtex 4 it was able to achieve real time video coding of 60 fps at 95.56 MHz CLK frequency.
In this paper, a novel parallelized implementation of rotation invariant texture classification u... more In this paper, a novel parallelized implementation of rotation invariant texture classification using Heterogeneous Computing Platforms like CPU and Graphics Processing Unit (GPU) is proposed. A complete modeling of the LBP operator as well as its improvised versions of Complete Local Binary Patterns (CLBP) and Multi-scale Local Binary Patterns (MLBP) has been developed on a CPU and GPU based Heterogeneous computing platforms using OpenCL. The tests using these feature descriptors of Local Binary Pattern (LBP) algorithms and their parallelized implementation using OpenCL were also performed. Significant Improvement in computation speed is achieved over traditional CPU-based algorithms. To test the accuracy of the GPU implemented algorithms a set of textures were classified using selected LBP, CLBP and MLBP descriptors. Classification was performed by applying these descriptors to several unique texture classes at various spatial resolutions and rotations. The primary focus of this paper is to provide an overview of these algorithms, demonstrate observed performance gains and to verify the validity of using these descriptors for texture analysis on a CPU and GPU based Heterogeneous Platform.
Brain tumor, is one of the major causes for the increase in mortality among children and adults. ... more Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.
Abstract In the past years, microarray technologies have become a central tool in biological rese... more Abstract In the past years, microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern plays an important role in the analysis of genes. Besides traditional clustering methods, biclustering is also being used to analyze biological datasets due to its ability to group both genes across conditions simultaneously. The paper presents a comparison of advanced with the traditional tools for biological data extraction. This paper compares different ...
Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an import... more Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an important preprocessing step in neuroimaging analysis. There are many tools developed to handle human fMRI images, which could involve manual steps for verifying results from brain segmentation that makes it time consuming and inefficient. In this study, we will use the segment anything model (SAM), a freely available neural network released by Meta[4], which has shown promising results in many generic segmentation applications. We will analyze the efficiency of SAM for neuroimaging brain segmentation by removing skull artifacts. The results of the experiments showed promising results that explore using automated segmentation algorithms for neuroimaging without the need to train on custom medical imaging dataset.
Journal of Neurology Research Review & Reports, Nov 29, 2023
Reproducibility is a key component of scientific research, and its significance has been increasi... more Reproducibility is a key component of scientific research, and its significance has been increasingly recognized in the field of Neuroscience. This paper explores the origin, need, and benefits of reproducibility in Neuroscience research, as well as the current landscape surrounding this practice, and further adds how boundaries of current reproducibility should be expanded to computing infrastructure. The reproducibility movement stems from concerns about the credibility and reliability of scientific findings in various disciplines, including Neuroscience. The need for reproducibility arises from the importance of building a robust knowledge base and ensuring the reliability of research findings. Reproducible studies enable independent verification, reduce the dissemination of false or misleading results, and foster trust and integrity within the scientific community. Collaborative efforts and knowledge sharing are facilitated, leading to accelerated scientific progress and the translation of research into practical applications. On the data front, we have platforms such as openneuro for open data sharing, on the analysis front we have containerized processing pipelines published in public repos which are reusable. There are also platforms such as openneuro, NeuroCAAS, brainlife etc which caters to the need for a computing platform. However, along with benefits these platforms have limitations as only set types of processing pipelines can be run on the data. Also, in the world of data integrity and governance, it may not be far in the future that some countries may require to process the data within the boundaries limiting the usage of the platform. To introduce customized, scalable neuroscience research, alongside open data, containerized analysis open to all, we need a way to deploy cloud infrastructure required for the analysis with templates. These templates are a blueprint of infrastructure required for reproducible research/analysis in a form of code. This will empower anyone to deploy computational infrastructure on cloud and use data processing pipeline on their own infrastructure of their choice and magnitude. Just as docker files are created for any analysis software developed, an IAC template accompanied with any published analysis pipeline, will enable users to deploy infrastructure on cloud required to carry out analysis on their data.
Convolutional Neural Networks (CNNs) have greatly influenced the field of Embedded Vision and Edg... more Convolutional Neural Networks (CNNs) have greatly influenced the field of Embedded Vision and Edge Artificial Intelligence (AI), enabling powerful machine learning capabilities on resource-constrained devices. This article explores the relationship between CNN compute requirements and memory bandwidth in the context of Edge AI. We delve into the historical progression of CNN architectures, from the early pioneering models to the current state-of-the-art designs, highlighting the advancements in compute-intensive operations. We examine the impact of increasing model complexity on both computational requirements and memory access patterns. The paper presents a comparison analysis of the evolving trade-off between compute demands and memory bandwidth requirements in CNNs. This analysis provides insights into designing efficient architectures and potential hardware accelerators in enhancing CNN performance on edge devices.
Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implement... more Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provide accurate estimates of core body temperature and are preferred sites for fever screening. Therefore, rapid, automated identification of the canthi regions within facial IR images may greatly facilitate rapid fever screening of asymptomatic travelers. However, it is more difficult to accurately identify the canthi regions from IR images than from visible images that are rich with exploitable features. In this study, we developed and evaluated techniques for multi-modality image registration (MMIR) of simultaneously captured visible and IR facial images for fever screening. We used free form deformation (FFD) models based on edge maps to improve registration accuracy after an affine transformation. Two widely used FFD models in medical image registration based on the Demons and cubic B-spline algorithms were qualitatively compared. The results showed that the Demons algorithm outperformed the cubic B-spline algorithm, likely due to overfitting of outliers by the latter method. The quantitative measure of registration accuracy, obtained through selected control point correspondence, was within 2.8 ± 1.2 mm, which enables accurate and automatic localization of canthi regions in the IR images for temperature measurement.
Title of thesis: IR THERMOGRAPHY AND IMAGE PHOTOPLETHYSMOGRAPHY : NON-CONTACT MEDICAL COUNTER MEA... more Title of thesis: IR THERMOGRAPHY AND IMAGE PHOTOPLETHYSMOGRAPHY : NON-CONTACT MEDICAL COUNTER MEASURE FOR INFECTION SCREENING Yedukondala Narendra Dwith Chenna, Master of Science, 2017 Thesis directed by: Professor Dr. Rama Chellappa Department of Electrical and Computer Engineering Screening based on non-contact infrared thermometers (NCITs) and Infrared Thermography (IRTG) shows promising results for mass fever screening. IRTGs were found to be powerful, quick and non-invasive methods to detect elevated temperatures. In the case of temperature measurement using IRTGs, regions medically adjacent to inner canthi are preferred sites for fever screening (IEC 80601-2-59:2008), which show good stability and correlation with body temperature. However, detection of canthi in thermal images is challenging due to the absence of features unlike visible images, which have sharp features that can be used for eye corner detection. We use registration of thermal images with visible light images ...
This paper proposes an efficient VLSI architecture for the implementation of variable block size ... more This paper proposes an efficient VLSI architecture for the implementation of variable block size motion estimation (VBSME). To improve the performance video compression the Variable Block Size Motion Estimation (VBSME) is the critical path. Variable Block Size Motion Estimation feature has been introduced in to the H.264/AVC. This feature induces significant complexities into the design of the H.264/AVC video codec. This paper we compare the existing architectures for VBSME. An efficient architecture to improve the performance of Spiral Search for Variable Size Motion Estimation in H.264/AVC is proposed. Among various architectures available for VBSME spiral search provides hardware friendly data flow with efficient utilization of resources. The proposed implementation is verified using the MATLAB on foreman, coastguard and train sequences. The proposed Adaptive thresholding technique reduces the average number of computations significantly with negligible effect on the video qualit...
International Journal of Computer Applications, 2013
In this paper, a novel parallelized implementation of rotation invariant texture classification u... more In this paper, a novel parallelized implementation of rotation invariant texture classification using Heterogeneous Computing Platforms like CPU and Graphics Processing Unit (GPU) is proposed. A complete modeling of the LBP operator as well as its improvised versions of Complete Local Binary Patterns (CLBP) and Multi-scale Local Binary Patterns (MLBP) has been developed on a CPU and GPU based Heterogeneous computing platforms using OpenCL. The tests using these feature descriptors of Local Binary Pattern (LBP) algorithms and their parallelized implementation using OpenCL were also performed. Significant Improvement in computation speed is achieved over traditional CPU-based algorithms. To test the accuracy of the GPU implemented algorithms a set of textures were classified using selected LBP, CLBP and MLBP descriptors. Classification was performed by applying these descriptors to several unique texture classes at various spatial resolutions and rotations. The primary focus of this paper is to provide an overview of these algorithms, demonstrate observed performance gains and to verify the validity of using these descriptors for texture analysis on a CPU and GPU based Heterogeneous Platform.
Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tum... more Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. In this paper, we propose an efficient wavelet based algorithm for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. Hence this algorithm effectively uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the efficiency of tumor detection. We also evaluate the effectiveness of proposed algorithm on varying the wavelet fusion parameters like number of decompositions, type of wavelet used for the decomposition. The experimental results of the simulation on MRI and CT images show the performance efficiency of the proposed approach.
2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2012
Face recognition is the biometric identification of human's face and matching the image against a... more Face recognition is the biometric identification of human's face and matching the image against a library of known faces. The algorithm used to simulate the above is Eigen faces algorithm. The software which is been proposed to implement is OpenCL. OpenCL(Open Computing Language) is an open standard for general purpose parallel programming across CPUs, GPUs and other processors, is portable and provides efficient access to the power of these heterogeneous processing platforms. The OpenCL API has functions to identify devices, compile programs, send and receive information and run OpenCL programs on the chosen device. The proposed system is been simulated in MSVS (Microsoft visual studio 2010) using AMD APP SDK (Advanced Micro Devices Accelerated Parallel Processing Software Development Kit).
International Journal of Image, Graphics and Signal Processing, 2013
Brain tumor, is one of the major causes for the increase in mortality among children and adults. ... more Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.
This paper proposes an efficient VLSI architecture for the implementation of variable block size ... more This paper proposes an efficient VLSI architecture for the implementation of variable block size motion estimation (VBSME). To improve the performance video compression the Variable Block Size Motion Estimation (VBSME) is the critical path. Variable Block Size Motion Estimation feature has been introduced in to the H.264/AVC. This feature induces significant complexities into the design of the H.264/AVC video codec. This paper we compare the existing architectures for VBSME. An efficient architecture to improve the performance of Spiral Search for Variable Size Motion Estimation in H.264/AVC is proposed. Among various architectures available for VBSME spiral search provides hardware friendly data flow with efficient utilization of resources. The proposed implementation is verified using the MATLAB on foreman, coastguard and train sequences. The proposed Adaptive thresholding technique reduces the average number of computations significantly with negligible effect on the video quality. The results are verified using hardware implementation on Xilinx Virtex 4 it was able to achieve real time video coding of 60 fps at 95.56 MHz CLK frequency.
In this paper, a novel parallelized implementation of rotation invariant texture classification u... more In this paper, a novel parallelized implementation of rotation invariant texture classification using Heterogeneous Computing Platforms like CPU and Graphics Processing Unit (GPU) is proposed. A complete modeling of the LBP operator as well as its improvised versions of Complete Local Binary Patterns (CLBP) and Multi-scale Local Binary Patterns (MLBP) has been developed on a CPU and GPU based Heterogeneous computing platforms using OpenCL. The tests using these feature descriptors of Local Binary Pattern (LBP) algorithms and their parallelized implementation using OpenCL were also performed. Significant Improvement in computation speed is achieved over traditional CPU-based algorithms. To test the accuracy of the GPU implemented algorithms a set of textures were classified using selected LBP, CLBP and MLBP descriptors. Classification was performed by applying these descriptors to several unique texture classes at various spatial resolutions and rotations. The primary focus of this paper is to provide an overview of these algorithms, demonstrate observed performance gains and to verify the validity of using these descriptors for texture analysis on a CPU and GPU based Heterogeneous Platform.
Brain tumor, is one of the major causes for the increase in mortality among children and adults. ... more Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.
Abstract In the past years, microarray technologies have become a central tool in biological rese... more Abstract In the past years, microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern plays an important role in the analysis of genes. Besides traditional clustering methods, biclustering is also being used to analyze biological datasets due to its ability to group both genes across conditions simultaneously. The paper presents a comparison of advanced with the traditional tools for biological data extraction. This paper compares different ...
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