An Adaptive Liver Segmentation Method Based on Graph Cut and Intensity Statistics

Yong Xiong Sun; Li Ping Huang; Li Peng Liu; Qiu Yang Huang

doi:10.4028/www.scientific.net/AMR.846-847.1003

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 846-847An Adaptive Liver Segmentation Method Based on...

An Adaptive Liver Segmentation Method Based on Graph Cut and Intensity Statistics

Abstract:

An intensity statistics based graph cut segmentation algorithm is proposed in this paper to improve the accuracy and adaptive capacity of liver segmentation. The proposed segmentation method consists of four steps as follows: First, combined with the Otsu algorithm and associated with a cropped liver image, we defined a gray interval as the livers intensity range. Second, the fuzzy c-means clustering algorithm was applied to compute the average intensity and the variance. Third, we establish the cost function with the statistic results. Finally, we employed the improved graph cut model to extract the liver parenchyma from a large cross-section liver image. Experimental results show that the proposed segmentation method is feasible for different liver images of different intensity statistics.

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Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

1003-1006

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.846-847.1003

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Online since:

November 2013

Authors:

Yong Xiong Sun*, Li Ping Huang, Li Peng Liu, Qiu Yang Huang

Keywords:

Adaptive Segmentation, Graph Cut Model, Intensity Statistics, Liver Segmentation

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