Abstract: A novel approach to the segmentation of Multiple Sclerosis（MS） lesions in T2-weighted Magnetic Resonance（MR） images is presented.According to the characteristic of MS lesions show the same high brightness with CerebroSpinal Fluid（CSF） in T2-weighted images，combining the strengths of the kernel fuzzy C-means algorithm and morphology characteristics of MS lesion tissues，the segmentation of MS lesions based on kernel fuzzy C-means algorithm is presented.The modified kernel fuzzy C-means algorithm is used to basic segmentation.Then the MS lesions are extracted by morphological method.The MS segmentation in simulated T2-weighted MR images show that the proposed algorithm can provide a powerful segmentation.

Key words: segmentation of image, kernel fuzzy C-means, multiple sclerosis lesions

摘要： 提出了一种针对多发性硬化症病灶T2加权脑部磁共振（MR）图像的分割算法。根据多发性硬化症病灶和脑脊液在T2加权像上同表现为高亮度信号的特点，把模糊C均值分割算法与形态学方法相结合，提出了基于核模糊C均值的多发性硬化症病灶分割算法。该算法首先用改进的核模糊C均值算法做基础分割，再用形态学方法提取出多发性硬化症病灶得到最终分割结果。通过对多发性硬化症模拟脑部MR图像的分割结果表明，算法能够比较准确地分割多发性硬化症病灶。

关键词: 图像分割, 核模糊C均值, 多发性硬化症