Dr. Xinyu Lin Profile

Dr. Xinyu Lin

at Medicsight PLC

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Proceedings Article | 26 March 2007 Paper

Fully automatic segmentation of liver from multiphase liver CT

Yalin Zheng, Xiaoyun Yang, Xujiong Ye, Xinyu Lin

Proceedings Volume 6512, 65122X (2007) https://doi.org/10.1117/12.709711

KEYWORDS: Liver, Image segmentation, Computed tomography, Diffusion, Liver cancer, Algorithm development, Binary data, Abdomen, Image processing algorithms and systems, Computer aided diagnosis and therapy

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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%.

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