Abstract
Low contrast between tumor and healthy liver tissue is one of the significant and challenging features among others in the automated tumor delineation process. In this paper we propose kernel based clustering algorithms that incorporate Tsallis entropy to resolve long range interactions between tumor and healthy tissue intensities. This paper reports the algorithm and its encouraging results of evaluation with MICCAI liver Tumor Segmentation Challenge 08 (LTS08) dataset. Work in progress involves incorporating additional features and expert knowledge into clustering algorithm to improve the accuracy.
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Mandava, R., Yeow, L.S., Chandra, B.A., Haur, O.K., Pasha, M.F., Shuaib, I.L. (2012). Liver Tumor Segmentation Using Kernel-Based FGCM and PGCM. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_13
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DOI: https://doi.org/10.1007/978-3-642-28557-8_13
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