University of Arkansas at Little Rock

Research Assistant

Assisted in preparing and conducting in-depth research in the development and creation of data mining algorithms and image processing tools used to support tumor detection in biopsy images. Participated in evaluating large image datasets to identify trends and propose/recommend possible solutions based on the findings.

** Designed, developed, and created algorithm for graph mining in protein-protein interaction databases.

** Handpicked to serve as Research Intern on a special project for Department of IT Research at University of Arkansas for Medical Sciences in 2007.

University of Arkansas for Medical Sciences

Research Associate

Provided direct support in the development of advanced image processing tools and algorithms used to collect, manage, analyze, and present biological and genomic data for IT Research and Cardiology Departments. Worked closely with IT Research team to evaluate and test new software tools and provide guidance regarding needed changes and bug fixes.

** Designed and implemented an online database for Department of Cardiology to obtain a federal accreditation.

** Served as Adjunct Faculty member in the Department of Applied Science.

Texas A&M University-Commerce

Associate Professor (Tenured)

Recruited to assist in development and instruction of 15+ undergraduate/graduate courses, including neural networks, parallel computing in Bioinformatics, database systems and structures, and mobile programming. Prepared course materials, project assignments, and conducted student lab exercises. Administered, complied and submitted final grades. Maintain open office hours to assist students, one-on-one, in understanding/mastering complex processes.

** Created student centered lessons that encourage students to higher levels of critical thinking and analysis.

** Utilize hands-on research skills to deliver new updated material, facilitating/moderating open discussions.

** Conduct deep machine learning/data mining research and developed machine learning algorithms used to gather, analyze, and classify biomedical data sets.

** Successfully mastered development and use of predictive models for allocation of organ transplantation.

** Recognized by Top Journal as thought leader and regarding machine learning and data mining.

** Authored and won a $200K grant proposal from national and state agencies to fund data mining research.

** Credited for writing and published 60+ peer-reviewed journal and conference articles.

** Drove process to design, develop, and code a remote health monitoring iOS application.

** Awarded US Patent for developing Image processing apparatus and method for histological analysis.

** Prepared and performed 5+ presentations at multiple international academic conferences and symposiums.

** Achieved 500+ citations on Google Scholar with h-index of 13.

** Supervised development of 7 graduate master theses and one undergraduate honor thesis research projects.

** Honored with the Teaching Excellence award for entire Texas A&M University System, November 2011.

** Initiated Apple University Education Agreement and Oracle University Certification.

** Managed Microsoft Imagine program since 2015

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Induction regimen and survival in simultaneous heart-kidney transplant recipients

The Journal of Heart and Lung Transplantation

Background: Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). Methods : We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Induction regimen and survival in simultaneous heart-kidney transplant recipients

The Journal of Heart and Lung Transplantation

Background: Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). Methods : We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was

Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival

Kidney international reports

Introduction The survival benefit from simultaneous liver-kidney transplantation (SLK) over liver transplant alone (LTA) in recipients with moderate renal dysfunction is not well understood. Moreover, the impact of deceased donor organ quality in SLK survival has not been well described in the literature. Methods The Scientific Registry of Transplant Recipients was studied for adult recipients receiving LTA (N = 2700) or SLK (N = 1361) with moderate renal insufficiency between 2003 and 2013. The study cohort was stratified into 4 groups based on serum creatinine (<2 mg/dl versus ≥2 mg/dl) and dialysis status at listing and transplant. The patients with end-stage renal disease and requiring acute dialysis more than 3 months before transplantation were excluded. A propensity score matching was performed in each stratified group to factor out imbalances between the SLK and LTA regarding covariate distribution and to reduce measured confounding. Donor quality was assessed with liver donor risk index. The primary outcome of interest was posttransplant mortality. Results In multivariable propensity score-matched Cox proportional hazard models, SLK led to decrease in posttransplant mortality compared with LTA across all 4 groups, but only reached statistical significance (hazard ratio 0.77; 95% confidence interval, 0.62–0.96) in the recipients not exposed to dialysis and serum creatinine ≥ 2 mg/dl at transplant (mortality incidence rate per patient-year 5.7% in SLK vs. 7.6% in LTA, P = 0.005). The decrease in mortality was observed among SLK recipients with better quality donors (liver donor risk index < 1.5). Discussion Exposure to pretransplantation dialysis and donor quality affected overall survival among SLK recipients.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Induction regimen and survival in simultaneous heart-kidney transplant recipients

The Journal of Heart and Lung Transplantation

Background: Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). Methods : We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was

Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival

Kidney international reports

Introduction The survival benefit from simultaneous liver-kidney transplantation (SLK) over liver transplant alone (LTA) in recipients with moderate renal dysfunction is not well understood. Moreover, the impact of deceased donor organ quality in SLK survival has not been well described in the literature. Methods The Scientific Registry of Transplant Recipients was studied for adult recipients receiving LTA (N = 2700) or SLK (N = 1361) with moderate renal insufficiency between 2003 and 2013. The study cohort was stratified into 4 groups based on serum creatinine (<2 mg/dl versus ≥2 mg/dl) and dialysis status at listing and transplant. The patients with end-stage renal disease and requiring acute dialysis more than 3 months before transplantation were excluded. A propensity score matching was performed in each stratified group to factor out imbalances between the SLK and LTA regarding covariate distribution and to reduce measured confounding. Donor quality was assessed with liver donor risk index. The primary outcome of interest was posttransplant mortality. Results In multivariable propensity score-matched Cox proportional hazard models, SLK led to decrease in posttransplant mortality compared with LTA across all 4 groups, but only reached statistical significance (hazard ratio 0.77; 95% confidence interval, 0.62–0.96) in the recipients not exposed to dialysis and serum creatinine ≥ 2 mg/dl at transplant (mortality incidence rate per patient-year 5.7% in SLK vs. 7.6% in LTA, P = 0.005). The decrease in mortality was observed among SLK recipients with better quality donors (liver donor risk index < 1.5). Discussion Exposure to pretransplantation dialysis and donor quality affected overall survival among SLK recipients.

Fractals for Malignancy Detection in Dermoscopy Images

IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015)

Accurate diagnosis of melanocytic lesions is amongst the most difficult problems for dermatologists. Border irregularity of a skin lesion is one of the diagnostic criteria to be assessed by the dermatologist. While there are myriad publications defining the dermatologic criteria that reproducibly distinguish ("presumably") benign melanocytic nevi from malignant melanomas, these criteria are neither universally accepted nor easily recognizable in all cases. To close this gap, this study focuses on quantitative assessment of shape-based irregularity features of suspected skin lesions in dermoscopy images. Border irregularities were investigated and analyzed in 100 skin lesions to develop objective and quantifiable criteria that evaluate diagnostically challenging lesions and effectively distinguish benign from malignant lesions. More specifically, this study automatically delineates skin lesion borders and then quantitatively …

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Induction regimen and survival in simultaneous heart-kidney transplant recipients

The Journal of Heart and Lung Transplantation

Background: Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). Methods : We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was

Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival

Kidney international reports

Introduction The survival benefit from simultaneous liver-kidney transplantation (SLK) over liver transplant alone (LTA) in recipients with moderate renal dysfunction is not well understood. Moreover, the impact of deceased donor organ quality in SLK survival has not been well described in the literature. Methods The Scientific Registry of Transplant Recipients was studied for adult recipients receiving LTA (N = 2700) or SLK (N = 1361) with moderate renal insufficiency between 2003 and 2013. The study cohort was stratified into 4 groups based on serum creatinine (<2 mg/dl versus ≥2 mg/dl) and dialysis status at listing and transplant. The patients with end-stage renal disease and requiring acute dialysis more than 3 months before transplantation were excluded. A propensity score matching was performed in each stratified group to factor out imbalances between the SLK and LTA regarding covariate distribution and to reduce measured confounding. Donor quality was assessed with liver donor risk index. The primary outcome of interest was posttransplant mortality. Results In multivariable propensity score-matched Cox proportional hazard models, SLK led to decrease in posttransplant mortality compared with LTA across all 4 groups, but only reached statistical significance (hazard ratio 0.77; 95% confidence interval, 0.62–0.96) in the recipients not exposed to dialysis and serum creatinine ≥ 2 mg/dl at transplant (mortality incidence rate per patient-year 5.7% in SLK vs. 7.6% in LTA, P = 0.005). The decrease in mortality was observed among SLK recipients with better quality donors (liver donor risk index < 1.5). Discussion Exposure to pretransplantation dialysis and donor quality affected overall survival among SLK recipients.

Fractals for Malignancy Detection in Dermoscopy Images

IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015)

Accurate diagnosis of melanocytic lesions is amongst the most difficult problems for dermatologists. Border irregularity of a skin lesion is one of the diagnostic criteria to be assessed by the dermatologist. While there are myriad publications defining the dermatologic criteria that reproducibly distinguish ("presumably") benign melanocytic nevi from malignant melanomas, these criteria are neither universally accepted nor easily recognizable in all cases. To close this gap, this study focuses on quantitative assessment of shape-based irregularity features of suspected skin lesions in dermoscopy images. Border irregularities were investigated and analyzed in 100 skin lesions to develop objective and quantifiable criteria that evaluate diagnostically challenging lesions and effectively distinguish benign from malignant lesions. More specifically, this study automatically delineates skin lesion borders and then quantitatively …

A novel classification system for dysplastic nevus and malignant melanoma

IEEE International Conference on Image Processing (ICIP)

Melanoma is a potentially deadly form of skin cancer, however, if detected early, it is curable. A dysplastic nevus (atypical mole) is not cancerous but may represent a precursor to malignancy as nearly 40% of melanomas arise from a preexisting mole. In this study, we propose a system to classify a skin lesion image as melanoma (M), dysplastic nevus (D), and benign (B). For this purpose we develop a new two layered-system. The first layer consists of three binary Support Vector Machine (SVM) classifiers, one for each pair of classes, M vs B, M vs D, and B vs D. The second layer is a novel decision maker function, which uses probability memberships derived from the first layer. Each lesion is characterized with five features, which mostly overlaps with the ABCD rule of dermatology. The dataset we used have 112 lesions with 54 M, 38 D, and 20 B cases. In the experiments of melanoma detection, we obtained 98 …

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

BMC bioinformatics

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively.

Local edge-enhanced active contour for accurate skin lesion border detection

BMC Bioinformatics

Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Journal of neuroscience research

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs).

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

BMC bioinformatics

Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy

A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma

Texas Dermatological Society

Induction regimen and survival in simultaneous heart-kidney transplant recipients

The Journal of Heart and Lung Transplantation

Background: Induction therapy in simultaneous heart-kidney transplantation (SHKT) is not well studied in the setting of contemporary maintenance immunosuppression consisting of tacrolimus (TAC), mycophenolic acid (MPA), and prednisone (PRED). Methods : We analyzed the Organ Procurement and Transplant Network registry from January 1, 2000, to March 3, 2015, for recipients of SHKT (N = 623) maintained on TAC/MPA/PRED at hospital discharge. The study cohort was further stratified into 3 groups by induction choice: induction (n = 232), rabbit anti-thymoglobulin (r-ATG; n = 204), and interleukin-2 receptor-α (n = 187) antagonists. Survival rates were estimated using the Kaplan-Meier estimator. Multivariable inverse probability weighted Cox proportional hazard regression models were used to assess hazard ratios associated with post-transplant mortality as the primary outcome. The study cohort was

Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival

Kidney international reports

Introduction The survival benefit from simultaneous liver-kidney transplantation (SLK) over liver transplant alone (LTA) in recipients with moderate renal dysfunction is not well understood. Moreover, the impact of deceased donor organ quality in SLK survival has not been well described in the literature. Methods The Scientific Registry of Transplant Recipients was studied for adult recipients receiving LTA (N = 2700) or SLK (N = 1361) with moderate renal insufficiency between 2003 and 2013. The study cohort was stratified into 4 groups based on serum creatinine (<2 mg/dl versus ≥2 mg/dl) and dialysis status at listing and transplant. The patients with end-stage renal disease and requiring acute dialysis more than 3 months before transplantation were excluded. A propensity score matching was performed in each stratified group to factor out imbalances between the SLK and LTA regarding covariate distribution and to reduce measured confounding. Donor quality was assessed with liver donor risk index. The primary outcome of interest was posttransplant mortality. Results In multivariable propensity score-matched Cox proportional hazard models, SLK led to decrease in posttransplant mortality compared with LTA across all 4 groups, but only reached statistical significance (hazard ratio 0.77; 95% confidence interval, 0.62–0.96) in the recipients not exposed to dialysis and serum creatinine ≥ 2 mg/dl at transplant (mortality incidence rate per patient-year 5.7% in SLK vs. 7.6% in LTA, P = 0.005). The decrease in mortality was observed among SLK recipients with better quality donors (liver donor risk index < 1.5). Discussion Exposure to pretransplantation dialysis and donor quality affected overall survival among SLK recipients.

Fractals for Malignancy Detection in Dermoscopy Images

IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015)

Accurate diagnosis of melanocytic lesions is amongst the most difficult problems for dermatologists. Border irregularity of a skin lesion is one of the diagnostic criteria to be assessed by the dermatologist. While there are myriad publications defining the dermatologic criteria that reproducibly distinguish ("presumably") benign melanocytic nevi from malignant melanomas, these criteria are neither universally accepted nor easily recognizable in all cases. To close this gap, this study focuses on quantitative assessment of shape-based irregularity features of suspected skin lesions in dermoscopy images. Border irregularities were investigated and analyzed in 100 skin lesions to develop objective and quantifiable criteria that evaluate diagnostically challenging lesions and effectively distinguish benign from malignant lesions. More specifically, this study automatically delineates skin lesion borders and then quantitatively …

A novel classification system for dysplastic nevus and malignant melanoma

IEEE International Conference on Image Processing (ICIP)

Melanoma is a potentially deadly form of skin cancer, however, if detected early, it is curable. A dysplastic nevus (atypical mole) is not cancerous but may represent a precursor to malignancy as nearly 40% of melanomas arise from a preexisting mole. In this study, we propose a system to classify a skin lesion image as melanoma (M), dysplastic nevus (D), and benign (B). For this purpose we develop a new two layered-system. The first layer consists of three binary Support Vector Machine (SVM) classifiers, one for each pair of classes, M vs B, M vs D, and B vs D. The second layer is a novel decision maker function, which uses probability memberships derived from the first layer. Each lesion is characterized with five features, which mostly overlaps with the ABCD rule of dermatology. The dataset we used have 112 lesions with 54 M, 38 D, and 20 B cases. In the experiments of melanoma detection, we obtained 98 …

Hand Gesture Controlled Drones: An Open Source Library

IEEE, International Conference on Data Intelligence and Security (ICDIS),

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on