基于SOM-FCM的脑肿瘤图像分割算法

A brain tumor image segmentation algorithm based on SOM-FCM

为实现脑肿瘤图像的精确分割,提出一种SOM-FCM的脑肿瘤图像分割算法。对分块后图像进行模极大值边缘检测,将图像块分成边缘类和非边缘类,利用SOM神经网络对非边缘类聚类;将学习到的码本与FCM算法结合,对边缘类图像进行聚类,将2种聚类结果相结合,最终实现脑肿瘤图像的精确分割。实验结果表明,本算法分割精度优于现有2种算法,为脑肿瘤图像分割提供了一种有效方案。

In order to achieve accurate segmentation of brain tumor image,an adaptive brain tumor image segmentation algorithm is presented based on SOM neural network and FCM technique.Firstly,the segmented image is divided into small sub-blocks of the same size,the sub-block vectors are classified into edge pattern and non-edge pattern by using an edge detection algorithm.Then the non-edge pattern vectors are clustered by using SOM-based technique,the codebook is learned as the initial clustering centers of the FCM technique,which is used to process the edge pattern.The experimental results show that the segmentation accuracy is superior to the existing two algorithms,which provides an effective scheme for brain tumor image segmentation.