Marwa Ismail

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.

A computer aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images

Purpose: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. Methods: The proposed computer‐aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov‐Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non‐negativity‐constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers’ features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand‐drawn by retina experts. Results: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early‐stage DR, balanced between 40–79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave‐one‐out cross‐validation test for all the 52 subjects. Conclusion: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer‐assisted diagnostic system for early DR detection using the OCT retinal images.

Fully automated 3D colon segmentation for early detection of colorectal cancer based on convex formulation of the active contour model

Accurate colon segmentation would play a vital role in a virtual colonoscopy system, amounting for reliable polyp detection; a colon cancer indicator. In this paper, we propose a fully automated framework for 3D colon segmentation based on the global/convex continuous minimization of the active contour model in 3D space. For optimal results, 3D region growing and some colon anatomical features, e.g. size, persistence and curvature have been incorporated for post processing. The proposed framework, applied on 12 colon data sets, is compared with graph cuts (discrete optimization) and adaptive level sets (non-convex continuous optimization). Our results outperform the other two in different aspects including speed of convergence, sensitivity and specificity with overall accuracy of 99%.

Detection of white matter abnormalities in MR brain images for diagnosis of autism in children

This paper introduces a novel computer-aided diagnosis (CAD) system for the diagnosis of autism from magnetic resonance (MR) brain images of children. The proposed framework has two main components. First, cerebral white matter (CWM) is segmented from the brain volume using an adaptive shape model that is built from a set of co-aligned training images. A higher-order Markov Gibbs random field (MGRF) spatial interaction model is then integrated with an intensity model to account for data in homogeneities. Secondly, CWM meshes are reconstructed and a curvature-based analysis is applied in order to differentiate between autistic and control brains. From the reconstructed CWM meshes, we estimated 3 shape features (curvedness, sharpness, and mean curvature) that could geometrically describe CWM folds. The statistical analysis conducted on 20 autistic and control brains using binomial regression on raw features as well as on their principal components reveals that the three curvature-based measures could be used as discriminatory features between autistic and control brains. Moreover, the accuracy of the diagnostic results using K-nearest neighbor (KNN) classifier is 95%. These results show the promise of the proposed technique as a supplement to current diagnostic instruments (e.g., Autism Diagnostic Interview, Revised (ADI-R)).

3D automated colon segmentation for efficient polyp detection

With polyps being the main cause of colorectal cancer, accurate colon segmentation is a crucial step for polyp detection in a virtual colonoscopy system. This paper presents a fully automated segmentation framework for the colon which is based on convex formulation of the active contour model. Our approach is tested on 7 sets where the results are further validated for polyp detection. Results show the efficiency of the framework with an overall accuracy of 99%, and high sensitivity of polyp detection.

Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF

This paper introduces a new framework for the segmentation of different brain structures from 3D infant MR brain images. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on higher-order visual appearance characteristics of infant MRIs. These characteristics are described using voxel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of infant brain signals, a Linear Combination of Discrete Gaussians (LCDG) is used that has positive and negative components. Also to accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth-order families with a traditional second-order model is proposed. The proposed approach was tested on 40 in-vivo infant 3D MR brain scans, having their ground truth created by an expert radiologist, using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results promise an accurate segmentation of infant MR brain images compared to current open source segmentation tools.

Improved Colon Navigation for Efficient Polyp Detection in Virtual Colonoscopy

Colon cancer is a leading cause of death in the world and its early diagnosis highly increases the chances of survival. Virtual colonoscopy is a widely spreading technology that is used for polyp detection, the primary cause of colon cancer. This paper revisits an existing virtual colonoscopy technique, called Fly-over. It splits the colon into two halves along its centerline and assigns a camera to each half for navigation. While cutting the colon along its centerline increases the possibility of having missed polyps, the technique is re-visited here and the cutting framework is changed, which improved the rate of detection. Clinical validation was assessed by testing the navigation technique on several cases of real and synthetic challenging polyps versus other techniques. Fly-over technique provides efficient polyp detection of up to 100% with the least distortion rate.

A fast stochastic framework for automatic MR brain images segmentation

This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

A novel automatic segmentation of healthy and diseased retinal layers from OCT scans

This paper introduces a novel framework for segmenting retinal layers from optical coherence tomography (OCT) images. In order to account for the noise and inhomogeneity of OCT scans, especially for diseased ones, the proposed framework is based on unique joint model that combines shape, intensity, and spatial information, and is able to segment 12 distinct retinal layers. First, the shape prior is built using a subset of co-aligned training OCT images. The alignment process is initialized using an innovative method that employs multi-resolution edge tracking which defines control points on the tracked retinal boundaries. The shape model is then adapted during the segmentation process using visual appearance characteristics that are described using pixel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of OCT images, a linear combination of discrete Gaussians (LCDG) is used that has positive and negative components. Also, in order to accurately account for noise, the model is integrated with a second-order Markov Gibbs random field (MGRF) spatial interaction model. The proposed approach was tested on 200 normal and diseased OCT scans (e.g. Age macular degeneration (AMD), diabetic retinopathy), having their ground truth delineated by retina specialists, then measured using the Dice similarity coefficient (DSC), agreement coefficient (AC1), and average deviation (AD) metrics. The accuracy achieved by the segmentation approach clearly demonstrates the promise it holds for robust segmentation of retinal layers which would aid in the early diagnosis of different retinal abnormalities.