Ali Islam

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We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks(More)
Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without(More)
We present an original information theoretic measure of heart motion based on the Shannon's differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem, and as such, incorporation of(More)
OBJECTIVE The goal of this study is to investigate automatic myocardium tracking in cardiac Magnetic Resonance (MR) sequences using global distribution matching via level-set curve evolution. Rather than relying on the pixelwise information as in existing approaches, distribution matching compares intensity distributions, and consequently, is well-suited to(More)
This study investigates regional heart motion abnormality detection using various classifier features with Shannon's Differential Entropy (SDE). Rather than relying on elementary measurements or a fixed set of moments, the SDE measures global distribution information and, as such, has more discriminative power in classifying distributions. Based on(More)
Direct estimation of cardiac ventricular volumes has become increasingly popular and important in cardiac function analysis due to its effectiveness and efficiency by avoiding an intermediate segmentation step. However, existing methods rely on either intensive user inputs or problematic assumptions. To realize the full capacities of direct estimation, this(More)
Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characterization of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not sufficient due to the substantial variability in typical heart motion.(More)
This study investigates heart wall motion abnormality detection with an information theoretic measure of heart motion based on the Shannon's differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior(More)
Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for(More)
The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation,(More)