基于小波的磁共振图像聚类分割

目的 利用直方图自适应确定人体不同部位MRI的聚类类别的数目和相应的初始聚类中心,实现模糊-c均值聚类算法(FCM)分割的自适应。方法 首先采用小波变换拟合直方图的平滑包络线,降低噪声对寻找包络线极值的影响;其次根据微积分的知识求出包络线极大值的个数,按照文中给出的法则对包络线的极大值进行筛选,确定直方图中峰值的个数;最后以直方图中峰值的个数为聚类类别数,以相应的峰值为初始聚类中心,对MRI进行FCM分割。结果 采用该方法对多幅腹部和脑部MR图像进行分割,均能有效地自适应确定聚类的个数。结论 本文方法能够有效、准确地确定不同MR图像的聚类类别的个数,实现FCM的自适应。

Wavelet-based MR images clustering segmentation

Objective To determine the number of clustering categories of different MR T1WI adaptively using the histogram, and to achieve the adaptive segmentation by fuzzy c-means algorithm (FCM). Methods Firstly, the smooth histogram envelope was fitted through the wavelet transform, in order to alleviate the impact of noises on finding the extremes of the envelope. Secondly, the number of envelope maxima was found according to the knowledge of calculus, and then the maximums of the envelope were filtered in accordance with the rules given in the paper, thereby the number of peaks of the histogram would be determined. Then MR images were segmented through FCM for which the number of clustering categories was equal to histogram peak number and the centers of clustering categories were the corresponding histogram peaks. Results The number of clustering categories of multiple abdomen and brain MR image was determined effectively and adaptively with this method. Conclusion This method can effectively and accurately determine the number of the clustering categories of different MR images, and so achieve the adaptive of FCM.