[Tumor segmentation on multi-modality magnetic resonance images based on SVM model parameter optimization].
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.


RESULTS
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%.


CONCLUSIONS
The proposed method combines multi-modality images with SVM classifiers to allow accurate tumor image segmentation from MR images with a high precision.
Department of Electronic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. E-mail: wangxc90121@163.com.
Xiaochun
X
Wang
Jing
J
Huang
Feng
F
Yang
Man
M
Luo
D001932:Brain Neoplasms / Q000175:diagnosis*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D060388:Support Vector Machine