Abstract:ObjectiveTo explore an improved clustering segmentation algorithm and apply it on the automatic segmentation of brain MR image. Methods First, a novel colorization method is proposed to transform a gray brain MR into a color one and increase the image contrast of anatomical structure. Second, a probability density curve is drawn with gray histogram to split region at intersection points. Finally, the segmented image would be achieved by using the selected centroids in clustering method.ResultsDifferent segmentation algorithms are selected to conduct simulation experiment of the brain MR image. Qualitative evaluation results show that the proposed method can enhance contrast of gray matter, white matter and cerebrospinal fluid, and improve image quality. Quantitative evaluation results indicate the improved cluster algorithm generates a higher Jaccard coefficient than others, and has less computation time than other methods.ConclusionThe probabilistic line which is based on gray histogram can effectively avoid the blindness of the initial centroids selection, and make the segmented results more rapid and accurate. The proposed algorithm has higher clinical application value for the analysis of region of interest.
宋国权,李金锋. 基于聚类算法的脑部MR图像分割[J]. 中国医疗设备, 2017, 32(1): 26-29.
SONG Guo-quan, LI Jin-feng. Segmentation of Brian MR Image via Improved Clustering Algorithm. China Medical Devices, 2017, 32(1): 26-29.