Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Locally Constrained Watershed Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of multispectral images based on a fuzzy-possibilistic neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Image Processing
Comparison between immersion-based and toboggan-based watershed image segmentation
IEEE Transactions on Image Processing
International Journal of Applied Mathematics and Computer Science
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Hi-index | 0.00 |
Conventional watershed segmentation methods invariably produce over-segmented images due to the presence of noise or local irregularities in the source images. In this paper, a robust medical image segmentation technique is proposed, which combines watershed segmentation and the competitive Hopfield clustering network (CHCN) algorithm to minimize undesirable over-segmentation. In the proposed method, a region merging method is presented, which is based on employing the region adjacency graph (RAG) to improve the quality of watershed segmentation. The relation of inter-region similarities is then investigated using image mapping in the watershed and CHCN images to determine more appropriate region merging. The performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images. Significant and promising segmentation results were achieved on brain phantom simulated data.