基于支持向量机的磁共振脑组织图像分割

脑组织图像分割在医学图像分析中具有重要的理论和应用价值。由于支持向量机被看作是对传统学习分类器的一个好的替代,特别是在小样本、高维情况下,具有较好的泛化性能,因此可采用支持向量机方法对磁共振脑组织图像进行分割研究。为了验证支持向量机分割磁共振脑组织图像的效果,利用支持向量机进行了脑组织图像分割实验。实验结果表明:核函数及模型参数对支持向量机的分割性能有较大的影响;支持向量机方法适合作为小样本情况下的学习分类器;对目标边界模糊、目标灰度不均匀及目标不连续等情况下的图像(如医学图像)分割,支持向量机方法也是一个好的选择。

Segmentation of Magnetic Resonance Brain Tissues Image Based on Support Vector Machines

Segmentation of brain tissues is very important in medical image analysis.Support Vector Machines(SVM) is considered a good candidate because of its good generalization performance,especially for dataset with small number of samples in high demensional feature space.This paper investigates the segmentation of magnetic resonance brain tissues image based on SVM.Experimental results show that the influence of kernel function and model parameters on the generalization performance of SVM is significant;SVM is suitably used as learning classifier of small sample size;To segment targets with blurry edges,intensity non-uniformity and discontinuity(such as medical images),SVM approach is a good choice.