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.
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Zhou, J., Tian, Q., Chong, V., Xiong, W., Huang, W., Wang, Z. (2011). Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_17
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DOI: https://doi.org/10.1007/978-3-642-24319-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
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