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Proceedings Paper

Hierarchical clustering method for the segmentation of medical images
Author(s): Keiko Ohkura; Hidezaku Nishizawa; Takashi Obi; Masahiro Yamaguchi; Nagaaki Ohyama
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Paper Abstract

Analyzing medical images, which have been stored in digital information system day by day, is expected to make it possible to formulate knowledge useful for image diagnosis. In this paper, we propose a method for unsupervised medical image segmentation as the pre-processing of the analysis aiming to clear the relation between the image features and the possible outcome of a medical condition. In the proposed method, a square region around the every pixel is considered as a pattern vector, and a set of pattern vectors acquired from whole image are classified by using the technique of hierarchical clustering. In the hierarchical clustering, the set of pattern vectors is divided into two clusters at each node, according to the statistical criterion based on the entropy in thermodynamics. Results on the test image generated by the Markov Random Field model and the real medical images, photomicrographs of intestine, are shown.

Paper Details

Date Published: 24 June 1998
PDF: 8 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998);
Show Author Affiliations
Keiko Ohkura, Tokyo Institute of Technology (Japan)
Hidezaku Nishizawa, Tokyo Institute of Technology (Japan)
Takashi Obi, Tokyo Institute of Technology (Japan)
Masahiro Yamaguchi, Tokyo Institute of Technology (Japan)
Nagaaki Ohyama, Tokyo Institute of Technology (Japan)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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