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Proceedings Paper

An improved method for pancreas segmentation using SLIC and interactive region merging
Author(s): Liyuan Zhang; Huamin Yang; Weili Shi; Yu Miao; Qingliang Li; Fei He; Wei He; Yanfang Li; Huimao Zhang; Kensaku Mori; Zhengang Jiang
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Paper Abstract

Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.

Paper Details

Date Published: 3 March 2017
PDF: 12 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343H (3 March 2017); doi: 10.1117/12.2254366
Show Author Affiliations
Liyuan Zhang, Changchun Univ. of Science and Technology (China)
Huamin Yang, Changchun Univ. of Science and Technology (China)
Weili Shi, Changchun Univ. of Science and Technology (China)
Yu Miao, Changchun Univ. of Science and Technology (China)
Qingliang Li, Changchun Univ. of Science and Technology (China)
Fei He, Changchun Univ. of Science and Technology (China)
Wei He, Changchun Univ. of Science and Technology (China)
Yanfang Li, Changchun Univ. of Science and Technology (China)
Huimao Zhang, The First Hospital of Jilin Univ. (China)
Kensaku Mori, Nagoya Univ. (Japan)
Zhengang Jiang, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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