Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/917773
- Title
- A liver segmentation algorithm based on wavelets and machine learning
- Author/Creator
-
Luo, Suhai;
Jin, Jesse S.;
Chalup, Stephan K.;
Quin, Guoyu
- Description
- This paper introduces an automatic liver parenchyma segmentation algorithm that can delineate liver in abdominal CT images. The proposed approach consists of three main steps. Firstly, a texture analysis is applied onto input abdominal CT images to extract pixel level features. Here, two main categories of features, namely wavelet coefficients and Haralick texture descriptors are investigated. Secondly, support vector machines (SVM) are implemented to classify the data into pixel-wised liver or non-liver. Finally, specially combined morphological operations are designed as a post processor to remove noise and to delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present better classification than Haralick texture descriptors when SVMs are used; the other is that the combination of morphological operations with a pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and surgical planning systems. Examples of applying the algorithm on real CT data are presented with performance validation based on the automatically segmented results and that of manually segmented ones.
- Relation
- International Conference on Computational Intelligence and Natural Computing, 2009 (CINC '09). Proceedings of the International Conference on Computational Intelligence and Natural Computing, 2009 (CINC '09) (Wuhan, China 6-7 June, 2009)
- Publisher Link
- http://dx.doi.org/10.1109/CINC.2009.225
- Date
- 2009
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Keyword(s)
-
machine learning;
morphology;
segmentation;
texture analysis;
wavelet
- Resource Type
- conference paper
- Rights
- Copyright © 2009 IEEE. Reprinted from the Proceedings of the International Conference on Computational Intelligence and Natural Computing, 2009. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Identifier
- http://hdl.handle.net/1959.13/917773
- Identifier
- ISBN:9780769536453
- Language
- eng
- Reviewed
- Full Text
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