DocumentCode :
2376076
Title :
Automated recognition of the psoas major muscles on X-ray CT images
Author :
Kamiya, N. ; Zhou, X. ; Chen, H. ; Hara, T. ; Hoshi, H. ; Yokoyama, R. ; Kanematsu, M. ; Fujita, H.
Author_Institution :
Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
3557
Lastpage :
3560
Abstract :
The purpose of this study is to recognize the psoas major muscle on X-ray CT images. For this purpose, we propose a novel recognition method. The recognition process in this method involves three steps: the generation of a shape model for the psoas major muscle, recognition of anatomical points such as the origin and insertion, and the recognition of the psoas major muscles by the use of the shape model. We generated the shape model using 20 CT cases and tested the model for recognition in 20 other CT cases. The average Jaccard similarity coefficient (JSC) and reproducibility rate were 0.704 and 0.783, respectively. Experimental results indicate that our method was effective for a 2-D cross-sectional area (CSA) analysis.
Keywords :
computerised tomography; diagnostic radiography; image recognition; medical image processing; muscle; shape recognition; 2D cross-sectional area analysis; Jaccard similarity coefficient; X-ray CT images; automated recognition; psoas major muscles; reproducibility rate; shape model generation; Algorithms; Artificial Intelligence; Bone and Bones; Diagnosis, Computer-Assisted; Diagnostic Imaging; Female; Humans; Imaging, Three-Dimensional; Male; Pattern Recognition, Automated; Psoas Muscles; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Tomography, X-Ray Computed; X-Rays;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5332597
Filename :
5332597
Link To Document :
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