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In recent years, magnetic resonance imaging (MRI) has been explored for non-invasive assessment of renal transplant function. This paper proposes a computer-aided diagnostic (CAD) system for the assessment of renal transplant status, which integrates both clinical and MRI-derived biomarkers. The latter are derived from either 3D (2D + time) dynamic contrast-enhanced MRI or 4D (3D + b-value) diffusion-weighted...
A framework for 3D kidney segmentation from abdominal computed tomography (CT) images is proposed. Accurate kidney segmentation from CT images is a challenging task due to the large inhomogeneity of the kidney (e.g., cortex and medulla), inter-patient anatomical differences, etc. To account for these challenges, a novel framework utilizing random forest (RF) classification that has the ability to...
Although renal biopsy remains the gold standard for diagnosing the type of renal rejection, it is not preferred due to its invasiveness, recovery time (1–2 weeks), and potential for complications, e.g., bleeding and/or infection. Therefore, there is an urgent need to explore a non-invasive technique that can early classify renal rejection types. In this paper, we develop a computer-aided diagnostic...
The segmentation of the kidney tissues is a key step in developing any non-invasive computer-aided diagnostic (CAD) system for early detection of acute renal transplant rejection. This paper introduces a geometric (level-set)-based deformable model approach for the 3D kidney segmentation from diffusion-weighted magnetic resonance imaging (DW-MRI). The proposed deformable model is guided by a stochastic...
A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order...
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