@inproceedings{b3c15fddd9304c06bd3cdb6f668fe4e1,
title = "Delineation of liver tumors from CT scans using spectral clustering with out-of-sample extension and multi-windowing",
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.",
keywords = "CT, Spectral clustering, out-of-sample extension, tumor delineation",
author = "Jiayin Zhou and Weimin Huang and Wei Xiong and Wenyu Chen and Venkatesh, {Sudhakar K.} and Qi Tian",
year = "2012",
doi = "10.1007/978-3-642-33612-6_26",
language = "English (US)",
isbn = "9783642336119",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "246--254",
booktitle = "Abdominal Imaging",
note = "4th International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 01-10-2012",
}