- Title
- Improvement of liver segmentation by combining high order statistical texture features with anatomical structural features
- Creator
- Luo, Suhuai; Li, Xuechen; Li, Jiaming
- Relation
- Journal of Signal and Information Processing Vol. 5, Issue 5B, p. 67-72
- Publisher Link
- http://dx.doi.org/10.4236/eng.2013.55B014
- Publisher
- Scientific Research Publishing
- Resource Type
- journal article
- Date
- 2013
- Description
- Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robust-ness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an opti-mal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distribu-tions. Experiment results of liver segmentation on CT images using the proposed method are presented with perform-ance validation and discussion.
- Subject
- liver segmentation; texture feature; support vector machine; morphological operation
- Identifier
- http://hdl.handle.net/1959.13/1341416
- Identifier
- uon:28738
- Identifier
- ISSN:1947-3931
- Rights
- Copyright © 2013 Suhuai Luo, Xuechen Li, Jiaming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Language
- eng
- Full Text
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