Conference Paper

Multi-mode Narrow-band Thresholding with Application in Liver Segmentation from Low-contrast CT Images

Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
DOI: 10.1109/IIH-MSP.2009.78 Conference: Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Source: IEEE Xplore


Segmentation of liver in CT images is regarded as a challenge in image processing due to low-contrast of datasets, variety of liver shape, and its non-uniform texture; especially for abnormal cases. In this paper, we deal with normal and abnormal datasets as images containing two or more Gaussian components. We threshold a slice in a narrow band of each mode, find liver pixels based on a priori knowledge, prepare a probability map, and threshold the map to find initial liver border. Final boundary of liver is obtained through a few iterations of `Geodesic Active Contour'. The proposed method was tested on 30 normal and 17 abnormal datasets each containing 159-263 slices; acquired from different CT machines. The results for normal and abnormal datasets are completely acceptable, according to the evaluation done by a specialist. However, for severely abnormal datasets, the proposed method is regarded as a promising algorithm for liver segmentation.

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Available from: Yoshinobu Sato, Jan 09, 2014
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