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Proceedings Paper

Measuring glomerular number from kidney MRI images
Author(s): Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Berkay Kanberoglu; David Frakes; Kevin Bennett; Andreas Spanias
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Paper Abstract

Measuring the glomerular number in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. Commonly used approaches either require destruction of the entire kidney or perform extrapolation from measurements obtained from a few isolated sections. A recent magnetic resonance imaging (MRI) method, based on the injection of a contrast agent (cationic ferritin), has been used to effectively identify glomerular regions in the kidney. In this work, we propose a robust, accurate, and low-complexity method for estimating the number of glomeruli from such kidney MRI images. The proposed technique has a training phase and a low-complexity testing phase. In the training phase, organ segmentation is performed on a few expert-marked training images, and glomerular and non-glomerular image patches are extracted. Using non-local sparse coding to compute similarity and dissimilarity graphs between the patches, the subspace in which the glomerular regions can be discriminated from the rest are estimated. For novel test images, the image patches extracted after pre-processing are embedded using the discriminative subspace projections. The testing phase is of low computational complexity since it involves only matrix multiplications, clustering, and simple morphological operations. Preliminary results with MRI data obtained from five kidneys of rats show that the proposed non-invasive, low-complexity approach performs comparably to conventional approaches such as acid maceration and stereology.

Paper Details

Date Published: 21 March 2016
PDF: 9 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978412 (21 March 2016); doi: 10.1117/12.2216753
Show Author Affiliations
Jayaraman J. Thiagarajan, Lawrence Livermore National Lab. (United States)
Karthikeyan Natesan Ramamurthy, IBM Thomas J. Watson Research Ctr. (United States)
Berkay Kanberoglu, Arizona State Univ. (United States)
David Frakes, Arizona State Univ. (United States)
Kevin Bennett, Arizona State Univ. (United States)
Andreas Spanias, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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