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Proceedings Paper

Semi-automated segmentation of solid and GGO nodules in lung CT images using vessel-likelihood derived from local foreground structure
Author(s): Atsushi Yaguchi; Tomoya Okazaki; Tomoyuki Takeguchi; Sumiaki Matsumoto; Yoshiharu Ohno; Kota Aoyagi; Hitoshi Yamagata
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Paper Abstract

Reflecting global interest in lung cancer screening, considerable attention has been paid to automatic segmentation and volumetric measurement of lung nodules on CT. Ground glass opacity (GGO) nodules deserve special consideration in this context, since it has been reported that they are more likely to be malignant than solid nodules. However, due to relatively low contrast and indistinct boundaries of GGO nodules, segmentation is more difficult for GGO nodules compared with solid nodules. To overcome this difficulty, we propose a method for accurately segmenting not only solid nodules but also GGO nodules without prior information about nodule types. First, the histogram of CT values in pre-extracted lung regions is modeled by a Gaussian mixture model and a threshold value for including high-attenuation regions is computed. Second, after setting up a region of interest around the nodule seed point, foreground regions are extracted by using the threshold and quick-shift-based mode seeking. Finally, for separating vessels from the nodule, a vessel-likelihood map derived from elongatedness of foreground regions is computed, and a region growing scheme starting from the seed point is applied to the map with the aid of fast marching method. Experimental results using an anthropomorphic chest phantom showed that our method yielded generally lower volumetric measurement errors for both solid and GGO nodules compared with other methods reported in preceding studies conducted using similar technical settings. Also, our method allowed reasonable segmentation of GGO nodules in low-dose images and could be applied to clinical CT images including part-solid nodules.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142S (20 March 2015); doi: 10.1117/12.2080928
Show Author Affiliations
Atsushi Yaguchi, Toshiba Corp. (Japan)
Tomoya Okazaki, Toshiba Corp. (Japan)
Tomoyuki Takeguchi, Toshiba Corp. (Japan)
Sumiaki Matsumoto, Kobe Univ. Graduate School of Medicine (Japan)
Yoshiharu Ohno, Kobe Univ. Graduate School of Medicine (Japan)
Kota Aoyagi, Toshiba Medical Systems Corp. (Japan)
Hitoshi Yamagata, Toshiba Medical Systems Corp. (Japan)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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