An adaptive single seed based region growing algorithm for color image segmentation

In this paper an adaptive single seed based region growing algorithm (ASSRG) is proposed for color image segmentation. The proposed method starts with the center pixel of the image as the initial seed. The region growing formula uses three homogeneity criteria local, global and relative, in two steps to label the pixel to a region. It first checks for the color similarity of the pixel with respect to the connected labelled pixel and secondly with the mean value of a growing region. If the similarity criterion is fulfilled then this pixel is included in the growing region. Otherwise the similarity of the pixel with respect to its 8-neighbors is compared with respect to the mean value of a growing region. If the pixel is closer to the growing region as compared to its neighbors then it is included in the growing region, otherwise it is labelled as boundary pixel. After one region is completely grown, the next seed pixel is selected from the boundary pixel stack. Region merging is performed to reduce over segmentation in the results. We have applied our algorithm to Berkley images with successful results and the evaluation of the segmented images has been done using Liu's F-factor, total number of regions segmented and time taken by the algorithm. A fuzzy rule based modification of the algorithm is also proposed to further improve results. The proposed algorithm is also compared with SSRG algorithm using Otsu's threshold, SRGRM algorithm and MRG region growing techniques and is shown to outperform all methods.