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Proceedings Paper

Automated abdominal lymph node segmentation based on RST analysis and SVM
Author(s): Yukitaka Nimura; Yuichiro Hayashi; Takayuki Kitasaka; Kazuhiro Furukawa; Kazunari Misawa; Kensaku Mori
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Paper Abstract

This paper describes a segmentation method for abdominal lymph node (LN) using radial structure tensor analysis (RST) and support vector machine. LN analysis is one of crucial parts of lymphadenectomy, which is a surgical procedure to remove one or more LNs in order to evaluate them for the presence of cancer. Several works for automated LN detection and segmentation have been proposed. However, there are a lot of false positives (FPs). The proposed method consists of LN candidate segmentation and FP reduction. LN candidates are extracted using RST analysis in each voxel of CT scan. RST analysis can discriminate between difference local intensity structures without influence of surrounding structures. In FP reduction process, we eliminate FPs using support vector machine with shape and intensity information of the LN candidates. The experimental result reveals that the sensitivity of the proposed method was 82.0 % with 21.6 FPs/case.

Paper Details

Date Published: 24 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352U (24 March 2014); doi: 10.1117/12.2043349
Show Author Affiliations
Yukitaka Nimura, Nagoya Univ. (Japan)
Yuichiro Hayashi, Nagoya Univ. (Japan)
Takayuki Kitasaka, Aichi Institute of Technology (Japan)
Kazuhiro Furukawa, Nagoya Univ. (Japan)
Kazunari Misawa, Aichi Cancer Ctr. Hospital (Japan)
Kensaku Mori, Nagoya Univ. (Japan)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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