A Hybrid Region Growing Algorithm for Medical Image Segmentation

In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.