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.
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