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Modified Level Cut Liver Segmentation from CT

Publication Type : Conference Proceedings

Publisher : 9th International Conference on Advanced Computing

Source : 9th International Conference on Advanced Computing (ICoAC 2017), MIT, Chennai (2017)

Url : https://ieeexplore.ieee.org/abstract/document/8441362

Keywords : Histogram threshold tail method, Level Cut Segmentation, Liver Vessel Segmentation, Modified Level Cut Threshold Segmentation, Threshold segmentation

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2017

Abstract : For liver surgical planning accurate segmentation of liver vasculature from abdominal CTis very significant[39]. Study on the liver vessel structure helps to locate the tumor. But segmentation of CT Liver vasculature is not easy task because of artifacts, complex structure[11] of liver anatomy. The main two vasculature structures among the four different tubular systems are the portal(main) and hepatic veins inside liver[37]. It is not so easy to segment and differentiate among them because of the twist, curved, obstruct each other nature and sometimes distorted by liver lesions. Also poor contrast and imperfect volume effect adds difficulties on automatic vessel segmentation of liver. In this we present a novel work on automatic vasculature segmentation of Liver derived from modified level set method. Secondly Liver region is extracted from CT abdominal image using cropping. Finally, modified level cut method is used to segment vessels of liver from background voxels. Experimental outcome shows that the proposed technique can efficiently segment liver vessels from CT abdominal images. The proposed technique has been evaluated on 300 clinical CT images collected from HCG Cancer Research Hospital.

Cite this Research Publication : R. R. Nair, Dr. Tripty Singh, Nayar, R., and Kumar, S., “Modified Level Cut Liver Segmentation from CT”, 9th International Conference on Advanced Computing (ICoAC 2017). MIT, Chennai, 2017.

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