NDLI: A novel framework for automatic segmentation of kidney from DW-MRI

Content Provider	IEEE Xplore Digital Library
Author	Shehata, M. Khalifa, F. Soliman, A. Alrefai, R. Abou El-Ghar, M. Dwyer, A.C. Ouseph, R. El-Baz, A.
Copyright Year	2015
Description	Author affiliation: Kidney Transplantation Kidney Disease Center, Univ. of Louisville, Louisville, KY, USA (Dwyer, A.C.; Ouseph, R.) \|\| Bioeng. Dept., Univ. of Louisville, Louisville, KY, USA (Shehata, M.; Khalifa, F.; Soliman, A.; Alrefai, R.; El-Baz, A.) \|\| Radiol. Dept., Mansoura Univ., Mansoura, Egypt (Abou El-Ghar, M.)
Abstract	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 speed relationship based on an adaptive shape prior guided by the visual appearance of the DW-MRI data. The voxel-wise guiding of the level-sets is obtained by integrating these three image features into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated for 40 DW-MRI data sets acquired at b-values ranging from 0 to 1000 $s/mm^{2}$ and compared against other segmentation methods using three evaluation metrics: the Dice similarity coefficient (DSC), the 95-percentile modified Hausdorff distance, and the absolute kidney volume difference. Experimental results' evaluation between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed approach.
Starting Page	951
Ending Page	954
File Size	649960
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7164028
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Kidney Shape Magnetic resonance imaging Adaptation models Liver Three-dimensional displays B-Splines Renal Rejection Level-Set Segmentation Markov Random Field
Content Type	Text
Resource Type	Article

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