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Proceedings Paper

Automated recognition of the iliac muscle and modeling of muscle fiber direction in torso CT images
Author(s): N. Kamiya; X. Zhou; K. Azuma; C. Muramatsu; T. Hara; H. Fujita
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Paper Abstract

The iliac muscle is an important skeletal muscle related to ambulatory function. The muscles related to ambulatory function are the psoas major and iliac muscles, collectively defined as the iliopsoas muscle. We have proposed an automated recognition method of the iliac muscle. Muscle fibers of the iliac muscle have a characteristic running pattern. Therefore, we used 20 cases from a training database to model the movement of the muscle fibers of the iliac muscle. In the recognition process, the existing position of the iliac muscle was estimated by applying the muscle fiber model. To generate an approximation mask by using a muscle fiber model, a candidate region of the iliac muscle was obtained. Finally, the muscle region was identified by using values from the gray value and boundary information. The experiments were performed by using the 20 cases without abnormalities in the skeletal muscle for modeling. The recognition result in five cases obtained a 76.9% average concordance rate. In the visual evaluation, overextraction of other organs was not observed in 85% of the cases. Therefore, the proposed method is considered to be effective in the recognition of the initial region of the iliac muscle. In the future, we will integrate the recognition method of the psoas major muscle in developing an analytical technique for the iliopsoas area. Furthermore, development of a sophisticated muscle function analysis method is necessary.

Paper Details

Date Published: 24 March 2016
PDF: 4 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853K (24 March 2016); doi: 10.1117/12.2214613
Show Author Affiliations
N. Kamiya, Aichi Prefectural Univ. (Japan)
X. Zhou, Gifu Univ. School of Medicine (Japan)
K. Azuma, Univ. of Occupational and Environmental Health, Japan (Japan)
C. Muramatsu, Gifu Univ. School of Medicine (Japan)
T. Hara, Gifu Univ. School of Medicine (Japan)
H. Fujita, Gifu Univ. School of Medicine (Japan)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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