MULTI-ATLAS SEGMENTATION OF MR TUMOR BRAIN IMAGES USING LOW-RANK BASED IMAGE RECOVERY

Abstract

We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with a state-of-the-art method by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.

Existing System

Cost Function Masking (CFM), Low-Rank Plus Sparse Matrix Decomposition (LRSD)

Proposed System

In this paper, we propose a new low-rank method and integrate it into a new MAS framework for reliable and accurate segmentation of MR tumor brain images. Different from the conventional low-rank methods like LRSD, a new spatial constraint is introduced to impose different residual error constraints on tumor regions and normal brain regions, thus allowing effective recovery of tumor regions and also good preservation of normal brain regions in the resulting recovered images. Our MAS framework consists of two steps: 1) using the new low-rank method to get the recovered image from the input tumor brain image based on the information of normal brain atlases, and 2) registering each normal brain atlas to the recovered image. These two steps are iterated until convergence, for obtaining the final segmentation result.

Architecture

BLOCK DIAGRAM