南方医科大学学报Issue(5)：641-645,5.DOI:10.3969/j.issn.1673-4254.2014.05.09
基于SVM模型参数优化的多模态MRI图像肿瘤分割方法
Tumor segmentation on multi-modality magnetic resonance images based on SVM model parameter optimization
摘要
Abstract
Objective To develop a method for tumor segmentation on multi-modality magnetic resonance (MR) images based on parameter optimization of SVM model. Methods Each one of the 4 sub-classifiers was trained using the feature information in mono-modality MR images and applied to the corresponding modality images. The classification results differed due to different information in the selected support vectors of the mono-modality images. By modifying the weight values of the error data points, we chose the best weight values of the sub-classifier to obtain a weighed combination SVM classifier of multi-modalities for use in MR image segmentation. Result This tumor image segmentation method was validated on the MR images of brain tumors in 34 patients and resulted in an average classification accuracy of 90.59%. Compared with the 4 mono-modality classifiers, multi-modality RBF kernel SVM classifiers increased the overall accuracy by 5.76%-20.11%. Conclusion The proposed method combines multi-modality images with SVM classifiers to allow accurate tumor image segmentation from MR images with a high precision.关键词
多模态/混合核函数/支持向量机/肿瘤分割Key words
multi-modality/combined kernel function/support vector machine/tumor segmentation引用本文复制引用
王晓春,黄靖,杨丰,罗蔓..基于SVM模型参数优化的多模态MRI图像肿瘤分割方法[J].南方医科大学学报,2014,(5):641-645,5.基金项目
国家自然科学基金（61271155）；国家自然科学青年基金（81000642）Supported by National Natural Science Foundation of China (61271155) （61271155）
