Efficient Texture Segmentation by Hierarchical Multiple Markov Chain Model?

A novel multiscale texture model and a related algorithm for the unsupervised segmentation of medical images to locate tumors are proposed in this project. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ―fragmentation‖ step allows one to find the elementary textures of the model, while the ―reconstruction‖ step defines the hierarchical image segmentation based on a probabilistic measure which takes into account both region scale and inter-region interactions. The proposed algorithm provides robust and fast segmentation when compared to other algorithm and this project is used in medical science to trace the tumor.