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Proceedings Paper

Fully automatic segmentation of liver from multiphase liver CT
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Paper Abstract

Multidetector row CT, multiphase CT in particular, has been widely accepted as a sensitive imaging modality in the detection of liver cancer. Segmentation of liver from CT images is of great importance in terms of accurate detection of tumours, volume measurement, pre-surgical planning. The segmentation of liver, however, remains to be an unsolved problem due to the complicated nature of liver CT such as imaging noise, similar intensity to its adjacent structures and large variations of contrast kinetics and localised geometric features. The purpose of this paper is to present our newly developed algorithm aiming to tackle this problem. In our method, a CT image was first smoothed by geometric diffusion method; the smoothed image was segmented by thresholding operators. In order to gain optimal segmentation, a novel method was developed to choose threshold values based on both the anatomical knowledge and features of liver CT. Then morphological operators were applied to fill the holes in the generated binary image and to disconnect the liver from other unwanted adjoining structures. After this process, a so-called "2.5D region overlapping" filter was introduced to further remove unwanted regions. The resulting 3D region was regarded as the final segmentation of the liver region. This method was applied to venous phase CT data of 45 subjects (30 patient and 15 asymptomatic subjects). Our results show good agreement with the annotations delineated manually by radiologists and the overlapping ratio of volume is 87.7% on average and the correlation coefficient between them is 98.1%.

Paper Details

Date Published: 26 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122X (26 March 2007); doi: 10.1117/12.709711
Show Author Affiliations
Yalin Zheng, Medicsight PLC. (United Kingdom)
Xiaoyun Yang, Medicsight PLC. (United Kingdom)
Xujiong Ye, Medicsight PLC. (United Kingdom)
Xinyu Lin, Medicsight PLC. (United Kingdom)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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