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Proceedings Paper

Automatic screening of polycystic kidney disease in x-ray CT images of laboratory mice
Author(s): Shaun S. Gleason; Hamed Sari-Sarraf; Michael J. Paulus; Dabney K. Johnson; Mongi A. Abidi
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Paper Abstract

This paper describes the application of a statistical-based deformable model algorithm to the segmentation of kidneys in x-ray computed tomography (CT) images of laboratory mice. This segmentation algorithm has been developed as the crucial first step in a process to automatically screen mice for genetically-induced polycystic kidney disease (PKD). The algorithm is based on active shape models (ASMs) initially developed by Cootes, et al. Once the segmentation is complete, texture measurements are applied within kidney boundaries to detect the presence of PKD. The challenges associated with the segmentation of mouse kidneys (non-rigid organs) are presented, and the motivation for using ASMs in this application is discussed. Also, improvements were made to published ASM methods that may be generally helpful in other segmentation applications. In 15 of the 18 cases tested, the mouse kidneys and spine were detected with only minor errors in boundary position. In the remaining three cases, small parts of the kidneys were missed and/or some extra abdominal tissue was inadvertently included by the boundary. In all 18 cases, however, the kidneys were successfully detected at a level where PKD could be automatically screened for using mean-of-local-variance (MOLV) texture measurements.

Paper Details

Date Published: 6 June 2000
PDF: 10 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387747
Show Author Affiliations
Shaun S. Gleason, Oak Ridge National Lab. (United States)
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Michael J. Paulus, Oak Ridge National Lab. (United States)
Dabney K. Johnson, Oak Ridge National Lab. (United States)
Mongi A. Abidi, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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