Tissue Classification In MR Images Using Hierarchical Segmentation
Zhenyu Wu, Ryan P. Leahy · 1990 IEEE Nuclear Science Symposium Conference Record · 2005
Simple threshold and gradient based segmenta- tion algorithms are unable to provide reliable soft tissue clas- sification in MR images. To obtain improved estimates, we have developed a new unsupervised hierarchical segmentation algorithm upon which our tissue classification scheme is based. The MR images are modeled as a first order Gauss-Markov ran- dom field (GMRF) with unknown parameters. Through test- ing hypotheses of homogeneity for either labeled or unlabeled data, the segmentation algorithm seeks to group the pixels in- to connected regions which are homogeneous GMRFs. This is achieved in three steps. The image is represented by a quadtree and a split-and-merge procedure is applied to find large homo- geneous regions within the image. This is followed by a segmen- tation refinement step involving connected component labeling and region growing. Here, the use of a hierarchical approach allows the algorithm to learn about the image by first process- ing the easy-to-classify pixels while delaying decisions on the hard pixels until more information has been gathered. Final- ly, a novel graph theoretic clustering algorithm is developed to reduce the number of resulting connected regions. This clus- tering method is globally optimal for a properly defined family of cost functions.