基于边缘竞争模糊水平集的脑部磁共振图像自动分割

Improved fuzzy level set method for automatic MR image segmentation based on edge competition

针对脑部磁共振(MR)图像分割时容易出现的受噪声影响大和边缘定位模糊问题,提出一种以基于核函数的增强模糊C均值(RFCMK)算法结果为先验知识的边缘竞争水平集自动分割方法。首先采用RFCMK算法对图像进行预分割;然后对预分割后的各子类图像进行阈值化处理,并将其边缘作为水平集演化的初始轮廓;最后采用引入竞争机制的边缘指示器对各部分边缘进行演化。该方法对模拟图像不同层切面的分割实验表明,基于面积和基于边缘的评估统计值范围分别为[0.91,0.95]和[0.05,0.22]。对噪声图像的实验结果表明该方法能够有效地抑制噪声对分割结果的影响。

A level set segmentation method based on edge competition for automatic segmentation of brain Magnetic Resonance （MR） images was proposed, which employed the Robust Fuzzy C-Means Based Kernel Function （RFCMK） result as priori knowledge to solve the problems of noise and edge leakage. Firstly, the image was pre-segmented with the RFCMK algorithm. Then, the sub-class images derived from pre-segmentation were processed by threshold operation to get the edge, which was used as the initial contour for the level set evolution. Finally, a competition mechanism combining the gradient information of sub-class image and original image was introduced to the edge indicator. An image set from a simulated brain database and a real brain MR image were tested to validate the accuracy of the proposed method. The range of area - based and edge-based statistical value is [ 0.91, 0.95] and [ 0.05, 0.22], respectively. The experimental results show that the proposed method can detect the edge accurately, and reduce the effect of noise.