NDLI: Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method

Content Provider	IEEE Xplore Digital Library
Author	Dongxiang Chi Ying Zhao Ming Li
Copyright Year	2010
Description	Author affiliation: School of Electronics & Information, Shanghai Dianji University, Shanghai, China (Dongxiang Chi; Ying Zhao; Ming Li)
Abstract	Medical image segmentation plays an important role in medical visualization and diagnosis. We study in this paper an automatic segmentation method for liver magnetic resonance (MR) images based on the self-organizing map (SOM) and hierarchical agglomerative clustering method. At first, the local features of the MR image pixels are extracted to feed the SOM after a pre-processing step. The output prototypes are then filtered with the hits map and a hierarchical agglomerative clustering method is applied to the prototypes to select the best segmentation according to a quantitative image evaluation index. The segmentation results after the post-processing show the proposed method to be effective and promising. Further research work is also recommended.
Starting Page	1333
Ending Page	1337
File Size	1513924
Page Count	5
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5648009
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image segmentation Self-Organizing Maps Clustering methods Hierarchical Agglomerative Clustering Segmentation Prototypes Liver Liver MR Image Pixel Biomedical imaging
Content Type	Text
Resource Type	Article

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