Loading [MathJax]/extensions/MathZoom.js
Robert S. Keynton - IEEE Xplore Author Profile

Showing 1-25 of 79 results

Filter Results

Show

Results

Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functi...Show More
Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based ...Show More
A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the...Show More
A 3D deep learning-based convolution neural network (CNN) is developed for accurate segmentation of pathological bladder (both wall border and pathology) using T2-weighted magnetic resonance imaging (T2W-MRI). Our system starts with a preprocessing step for data normalization to a unique space and extraction of a region-of-interest (ROI). The major stage utilizes a 3D CNN for pathological bladder ...Show More
Autism is a developmental disorder associated with difficulties in communication and social interaction. Currently, the gold standard in autism diagnosis is the autism diagnostic observation schedule (ADOS) interviews that assign a score indicating the level of severity for each individual. However, current researchers investigate developing objective technologies to diagnose autism employing brai...Show More
Lung cancer leads deaths caused by cancer for both men and women worldwide, that is why creating systems for early diagnosis with machine learning algorithms and nominal user intervention is of huge importance. In this manuscript, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. This system integrates global and local features t...Show More
Autism is a developmental disorder associated with difficulties in communication and social interaction. Autism diagnostic observation schedule (ADOS) is considered the gold standard in autism diagnosis, which estimates a score explaining the severity level for each individual. Currently, brain image modalities are being investigated for the development of objective technologies to diagnose Autism...Show More
In this paper, a deep learning-based convolution neural network (CNN) is developed for accurate segmentation of the bladder wall using T2-weighted magnetic resonance imaging (T2W-MRI). Our framework utilizes a dual pathway, two-dimensional CNN for pathological bladder segmentation. Due to large bladder shape variability across subjects and the existence of pathology, a learnable adaptive shape pri...Show More
Autism is a complex neurological disorder which affects behavioral and communication skills. Numerous studies were presented suggesting abnormal development of neural networks in the brain in shape, functionality, and/or connectivity. While conventional diagnosis of autism is subjective and requires long time before confirmation, neuro-imaging techniques provide a promising alternative. This paper...Show More
A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV) is proposed in this paper. The method is composed of two steps. First, a fully convolutional U-net is used for the segmentation of the epi- and endo-cardial boundaries of the LV from cine MR images. This step incorporates a novel loss function that accounts for the class imbalance problem caused by t...Show More
Alzheimer's disease (AD) is one of the neurodegenerative diseases, an irreparable one, that targets the central nervous system and causes dementia. During its progression, the disease goes through a number of stages where the diagnosis of the disease at its early stage is highly recommended. Despite this recommendation, accomplishing this diagnosis task faces a number of obstacles including the va...Show More
This paper proposes a computer-aided diagnosis (CAD) system for localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI). This system uses DW-MRI data sets that were acquired at four b-values: 100, 200, 300, and 400 smm-2. The first step in the proposed system is prostate segmentation using a level set method. The evolution of this level set is guided not only by the ...Show More
Cardiac magnetic resonance imaging provides a way for heart's functional analysis. Through segmentation of the left ventricle from cardiac cine images, physiological parameters can be obtained. However, manual segmentation of the left ventricle requires significant time and effort. Therefore, automated segmentation of the left ventricle is the desired and practical alternative. This paper introduc...Show More
In this paper, a novel framework for global diagnosis of autism spectrum disorder (ASD) using task-based functional MRI data is presented. A speech fMRI experiment is held to obtain local features related to the functional activity of the brain. This study proposes both global diagnosis and local diagnosis by analyzing brain brainnetome atlas (BNT) which will lead to the first step of providing pe...Show More
Accurate segmentation of the bladder wall is of great importance for any computer-aided diagnostic system for bladder cancer (BC) detection and diagnosis. In this paper, a deep learning-based framework is developed for accurate segmentation of the bladder wall using T2-weighted magnetic resonance imaging (T2W-MRI). Our framework utilizes 3D convolution neural network (CNN) and incorporates context...Show More
Non-invasive evaluation of renal transplant function is essential to minimize and manage acute renal rejection (AR). A computer-assisted diagnostic (CAD) system is developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate r...Show More
The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at five distinct b-values. The first step in the proposed system depends on nonnegative matrix factorization (NMF) to fuse intensity features of prostate voxels, spatial features of neighboring voxels, and shap...Show More
Diabetic Retinopathy (DR) is one of the leading causes of blindness in working age population worldwide. DR is caused by high blood sugar levels (diabetes), which damages retinal blood vessels and leads to vision loss. The diagnosis of DR requires manual measurements and visual assessment of the changes that happen in the retina, which is highly complex task. Thus, there is an unmet clinical need ...Show More
Early diagnosis of pulmonary nodules is critical for lung cancer clinical management. In this paper, a novel framework for pulmonary nodule diagnosis, using descriptors extracted from single computed tomography (CT) scan, is introduced. This framework combines appearance and shape descriptors to give an indication of the nodule prior growth rate, which is the key point for diagnosis of lung nodule...Show More
Diabetic retinopathy (DR) is one of the major causes of blindness worldwide. It is a diabetes complication that occurs after the damage of the blood vessels in the light-sensitive tissue in the retina. So, early detection of DR could reduce the severity of the disease and help ophthalmologists in treating and investigating it more efficiently. In this paper, we developed a computer-aided diagnosis...Show More
A computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed in this paper. The proposed system starts by defining a region of interest that includes the prostate across the different slices of the input DWI volume. Then, the apparent diffusion coefficient (ADC) of the defined ROI is calculated, normalized and r...Show More
This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF)...Show More
Objective: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. Methods: This paper presents a computer-aided diagnostic (CAD) system for ear...Show More
In this paper, a computer-aided diagnosis (CAD) system for early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed. The proposed system begins with defining a region of interest that contains the prostate across the various slices of the input volume. Then, the apparent diffusion coefficient (ADC) of the defined region is calculated, normalized and r...Show More
Diabetic retinopathy (DR) is a leading cause of vision loss in adults between 20 and 74 years. It is one of the significant causes of blindness worldwide. It affects the blood vessels in the retina as a complication of diabetes. Early detection of DR could reduce the severity of the disease and help ophthalmologists in treating and investigating it more efficiently. In this paper, a computer-aided...Show More