Automatic Segmentation of Lung Areas Based on SNAKES and Extraction of Abnormal Areas

  • Authors:

  • Yoshinori Itai;Hyoungseop Kim;Seiji Ishikawa;Shigehiko Katsuragawa;Takayuki Ishida;Katsumi Nakamura;Akiyoshi Yamamoto

  • Affiliations:

  • Kyushu Institute of Technology;Kyushu Institute of Technology;Kyushu Institute of Technology;Kumamoto University;Hiroshima International University;Kyoaikai Tobata Kyoritsu Hospital;Kyoaikai Tobata Kyoritsu Hospital

  • Venue:
  • ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
  • Year:
  • 2005

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Abstract

Segmentation for lung areas from CT images is important tasks on understanding tissue construction, computing and extracting abnormal areas. Recently, many segmentation methods based on contour model are presented. SNAKES (active contour model), on the other hand, are used extensively in computer vision and image processing applications particularly to locate the object boundaries. In lung segmentation, SNAKES is used for extracting the detail of ROI. However, a completely automatic segmentation method is not yet published, since it needs some manual efforts for initial contouring and constructing the contour models. In this paper, we propose a segmentation method for lung areas based on SNAKES without considering any manual operations. Furthermore, abnormal area including ground-glass opacity or lung cancer is classified by voxel density on the CT slice set. Experiment is performed employing nine thorax CT image sets and satisfactory results are obtained. Obtained results are shown along with a discussion.