Segmentation of brain tumors in multi-parametric MR images via robust statistic information propagation

Abstract

A method is presented to segment brain tumors in multi-parametric MR images via robustly propagating reliable statistical tumor information which is extracted from training tumor images using a support vector machine (SVM) classification method. The propagation of reliable statistical tumor information is implemented using a graph theoretic approach to achieve tumor segmentation with local and global consistency. To limit information propagation between image voxels of different properties, image boundary information is used in conjunction with image intensity similarity and anatomical spatial proximity to define weights of graph edges. The proposed method has been applied to 3D multi-parametric MR images with tumors of different sizes and locations. Quantitative comparison results with state-of-the-art methods indicate that our method can achieve competitive tumor segmentation performance. © 2011 Springer-Verlag Berlin Heidelberg.