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. © 2012 Springer-Verlag.
CITATION STYLE
Zhou, J., Huang, W., Xiong, W., Chen, W., Venkatesh, S. K., & Tian, Q. (2012). Delineation of liver tumors from CT scans using spectral clustering with out-of-sample extension and multi-windowing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7601 LNCS, pp. 246–254). https://doi.org/10.1007/978-3-642-33612-6_26
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