Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features

Suhuai Luo, Xuechen Li, Jiaming Li

The CSIRO ICT Centre, Australia.
The University of Newcastle, Australia.

DOI: 10.4236/eng.2013.55B014                

Abstract

Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. 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 optimal 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 distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.

Keywords

Liver Segmentation; Texture Feature; Support Vector machine; Morphological Operation

Conflicts of Interest

The authors declare no conflicts of interest.

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