Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Brain Tumor Segmentation Using Support Vector Machines
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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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.