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Title: Segmentation of skull base tumors from MRI using a hybrid support vector machine-based method
Authors: Zhou, J.
Tian, Q.
Chong, V. 
Xiong, W.
Huang, W.
Wang, Z.
Issue Date: 2011
Abstract: To achieve robust classification performance of support vector machine (SVM), it is essential to have balanced and representative samples for both positive and negative classes. A novel three-stage hybrid SVM (HSVM) is proposed and applied for the segmentation of skull base tumor. The main idea of the method is to construct an online hybrid support vector classifier (HSVC), which is a seamless and nature connection of one-class and binary SVMs, by a boosting tool. An initial tumor region was first pre-segmented by a one-class SVC (OSVC). Then the boosting tool was employed to automatically generate the negative (non-tumor) samples, according to certain criteria. Subsequently the pre-segmented initial tumor region and the non-tumor samples were used to train a binary SVC (BSVC). By the trained BSVC, the final tumor lesion was segmented out. This method was tested on 13 MR images data sets. Quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than OSVC and BSVC. © 2011 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN: 9783642243189
ISSN: 03029743
DOI: 10.1007/978-3-642-24319-6_17
Appears in Collections:Staff Publications

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