Watershed segmentation with automatic altitude selection and region merging based on the markov random field model

Watershed transformation has proven to be an important tool in image analysis. However, the resulting image of watershed transformation is inevitably over-segmented due to the presence of noise or local irregularities in the input image. In this paper, the use of contour altitude at the immersion stage is proposed. Block gradient information computed from the input gradient image is defined and used to obtain a critical altitude of watershed flooding. This altitude is then refined based on entropy estimated from the intermediate segmentation result. Thereafter, an optimal altitude and its corresponding segmentation result can be obtained. Although this process can favorably reduce the number of regions, the quality of segmentation still requires further improvement. Hence, a Markov Random Field (MRF) model defined on a region adjacency graph (RAG) is adopted. Because the MRF model can merge neighboring regions that are similar in local statistic properties, it thus alleviates the over-segmentation problem and improves the quality of image segmentation. In the experimental studies, the proposed method has been tested using several benchmark images. It achieves improved appearance and energy indices in comparison with the results obtained by conventional methods.