Delineation of liver tumors from CT scans using spectral clustering with out-of-sample extension and multi-windowing

Authors:
Jiayin Zhou;Weimin Huang;Wei Xiong;Wenyu Chen;Sudhakar K. Venkatesh;Qi Tian
Affiliations:
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore;Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore;Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore;Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore;Department of Radiology, Mayo Clinic, Rochester, Minnesota;Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
Venue:
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
Year:
2012

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Abstract

Accurate extraction of live tumors from CT data is important for disease management. In this study, an algorithm based on spectral clustering with out-of-sample extension is developed for the semi-automated delineation of liver tumors from 3D CT scans. In this method, spatial information is incorporated into a similarity metric together with low-level image features. A trick of out-of-sample extension is performed to reduce the computational burden in eigen-decomposition for a large matrix. Experimental results show that the developed method using multi-windowing feature obtained better results than using only the original data-depth and the support vector machine method, with a sensitivity of 0.77 and a Jaccard similarity measure of 0.70.