Brain MR image segmentation by unifying level set and region growth
Binrong Ma · Beijing shengwu yixue gongcheng · 2007
An MR image segmentation method is presented by unifying level set and region growth. It is discussed that different arithmetic separate different tissues based on the imaging characters and tissue structures. First, separated the skull and Cerebrospinal (CSF) from the MR image using developed level set. Secone, the approximate gray-value of gray matter (GM) and white matter (WM) were achieved using histogram. After the seeds were located automatically, region growth was used to separate white matter from gray matter. Experimental results indicated this approach made full use of the region information and boundary information of MR images. Compared with traditional methods using singular algorithm to separate a brain MR image, this method is characterized by robustness and accurateness.