A multi-phase semi-automatic approach for multisequence brain tumor image segmentation

Abstract

Accurate Magnetic Resonance Imaging (MRI) image segmentation is a clinically challenging task. More often than not, one type of MRI image is insufficient to provide the complete information about a pathological tissue or a visual object from the image. As a result, radiology experts often combine multisequence images of a patient to verify the location, extension, prognosis and diagnosis of an object. There are mainly two challenges in medical image segmentation. One is ambiguous boundary that appears between an object and its neighboring region, and the other is intensity inhomogeneity that appears within a region. Thus, this paper focuses on how to effectively segment multisequence medical images despite these two main challenges. This paper proposes a multi-phase approach that integrates both data and domain knowledge into multisequence MR image segmentation. This study divides the segmentation approach into three phases, which are (i) information modeling, (ii), information fusion, and (iii) visual object extraction. In the first phase, random walks algorithm is modified and used to model the information of an image. Because of the ambiguous boundary and intensity inhomogeneity that appear within an image, extra terms related to homogeneity- and object feature-based components are added into the weighting function of random walks algorithm. In the second phase, weighted averaging method is used to fuse information from the image sequences. Both data information of an image as well as user knowledge are integrated to determine the weights of each sequence for fusion. In the final phase, the concept of information theoretic rough sets (ITRS) is utilized to address the issue of ambiguous boundary that may appear between the visual object and its background for object extraction. The proposed approach is tested on MICCAI brain tumor dataset to extract brain tumor and its performance is compared with other established methods. The experiments show promising results, with an average DICE accuracy of 0.7 and 0.63 for high- and low-grade tumor, respectively. As compared to the other fully- and semi-automatic methods that require training and careful initialization processes, the proposed approach is able to extract the brain tumor with prior knowledge about the image.