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

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6 Author(s)
Foruzan, A.H. ; Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan ; Yen-Wei Chen ; Zoroofi, R.A. ; Furukawa, A.
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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.

Published in:

Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on

Date of Conference:

12-14 Sept. 2009