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Proceedings Paper • Open Access

Automated segmentation and recognition of the bone structure in non-contrast torso CT images using implicit anatomical knowledge
Author(s): X. Zhou; T. Hayashi; M. Han; H. Chen; T. Hara; H. Fujita; R. Yokoyama; M. Kanematsu; H. Hoshi

Paper Abstract

X-ray CT images have been widely used in clinical diagnosis in recent years. A modern CT scanner can generate about 1000 CT slices to show the details of all the human organs within 30 seconds. However, CT image interpretations (viewing 500-1000 slices of CT images manually in front of a screen or films for each patient) require a lot of time and energy. Therefore, computer-aided diagnosis (CAD) systems that can support CT image interpretations are strongly anticipated. Automated recognition of the anatomical structures in CT images is a basic pre-processing of the CAD system. The bone structure is a part of anatomical structures and very useful to act as the landmarks for predictions of the other different organ positions. However, the automated recognition of the bone structure is still a challenging issue. This research proposes an automated scheme for segmenting the bone regions and recognizing the bone structure in noncontrast torso CT images. The proposed scheme was applied to 48 torso CT cases and a subjective evaluation for the experimental results was carried out by an anatomical expert following the anatomical definition. The experimental results showed that the bone structure in 90% CT cases have been recognized correctly. For quantitative evaluation, automated recognition results were compared to manual inputs of bones of lower limb created by an anatomical expert on 10 randomly selected CT cases. The error (maximum distance in 3D) between the recognition results and manual inputs distributed from 3-8 mm in different parts of the bone regions.

Paper Details

Date Published: 27 March 2009
PDF: 4 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593S (27 March 2009); doi: 10.1117/12.812945
Show Author Affiliations
X. Zhou, Gifu Univ. School of Medicine (Japan)
T. Hayashi, Gifu Univ. School of Medicine (Japan)
M. Han, Gifu Univ. School of Medicine (Japan)
H. Chen, Gifu Univ. School of Medicine (Japan)
T. Hara, Gifu Univ. School of Medicine (Japan)
H. Fujita, Gifu Univ. School of Medicine (Japan)
R. Yokoyama, Gifu Univ. Hospital (Japan)
M. Kanematsu, Gifu Univ. Hospital (Japan)
H. Hoshi, Gifu Univ. School of Medicine (Japan)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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