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Proceedings Paper

A novel Hessian based algorithm for rat kidney glomerulus detection in 3D MRI
Author(s): Min Zhang; Teresa Wu; Kevin M. Bennett
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Paper Abstract

The glomeruli of the kidney perform the key role of blood filtration and the number of glomeruli in a kidney is correlated with susceptibility to chronic kidney disease and chronic cardiovascular disease. This motivates the development of new technology using magnetic resonance imaging (MRI) to measure the number of glomeruli and nephrons in vivo. However, there is currently a lack of computationally efficient techniques to perform fast, reliable and accurate counts of glomeruli in MR images due to the issues inherent in MRI, such as acquisition noise, partial volume effects (the mixture of several tissue signals in a voxel) and bias field (spatial intensity inhomogeneity). Such challenges are particularly severe because the glomeruli are very small, (in our case, a MRI image is ~16 million voxels, each glomerulus is in the size of 8~20 voxels), and the number of glomeruli is very large. To address this, we have developed an efficient Hessian based Difference of Gaussians (HDoG) detector to identify the glomeruli on 3D rat MR images. The image is first smoothed via DoG followed by the Hessian process to pre-segment and delineate the boundary of the glomerulus candidates. This then provides a basis to extract regional features used in an unsupervised clustering algorithm, completing segmentation by removing the false identifications occurred in the pre-segmentation. The experimental results show that Hessian based DoG has the potential to automatically detect glomeruli,from MRI in 3D, enabling new measurements of renal microstructure and pathology in preclinical and clinical studies.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132N (20 March 2015); doi: 10.1117/12.2081484
Show Author Affiliations
Min Zhang, Arizona State Univ. (United States)
Mayo Clinic Arizona (United States)
Teresa Wu, Arizona State Univ. (United States)
Mayo Clinic Arizona (United States)
Northeastern Univ. (China)
Kevin M. Bennett, Univ. of Hawai'i at Manoa (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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