Summary

The problem of tumorous tissues segmentation of MR brain images: • Tumorous tissues vary in size, shape and location. • They are also accompanied with edema, hemorrhage, necrosis and cystic components. • The boundaries of the tumorous tissues may be blurry. • There are a great many pixels (such as 256 × 256 × 124) for 3D MR images. • Segmentation is high computational complexity and large memory requirements. Methods of Segmentation We regard the segmentation problem as a classification problem. Supervised and unsupervised methods: • Supervised methods require the scrupulous labeling work by doctors or experts, which is time consuming and costly. • Unsupervised methods are difficult to produce good result fully automatically. When the tumorous tissues are very small in the image, the unsupervised methods will attempt to segment the normal tissues, such as: gray matter, white matter and the cerebrospinal fluid. Our semi-supervised inductive methods: • Our approach uses the labeled data in one image and a subset of unlabeled data to classify the remains. • It can segment 3D data by sampling the unlabeled data from 3D images rather than by segmenting 2D images sequently. • This method is related to semi-automated segmentation and interactive segmentation. MRF and GRF Most of the algorithms in Bayesian framework focus on the joint distribution P (xi, ti). For the algorithms that do not need to predict for the unobserved data, there are mainly two ways to model this probability: a) : P (X N, t N) = P (X N|t N)P (t N) b) : P (X N, t N) = P (t N|X N)P (X N) This will lead to the following models