Brain tumor segmentation of normal and pathological tissues using K-mean clustering with fuzzy C-mean clustering

Abstract

Segmentation of brain tumor from magnetic resonance imaging is a time consuming and critical task due to unpredictable characteristics of tumor tissues. In this paper, we propose a new tissue segmentation algorithm that segments brain MR images into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), tumor and edema. It is crucial to segment the normal and pathological tissues simultaneously for treatment planning. K-mean clustering algorithm has minimal computation time, and fuzzy c mean clustering has advantages in the aspect of accuracy on the soft tissues. So we are integrating the K-mean clustering algorithm with Fuzzy C-means clustering algorithm for segmenting the brain magnetic resonance imaging. First, we segment the abnormal region from T2-weighted FLAIR modality based on k mean clustering algorithm integrated with fuzzy c mean algorithm. And in the next stage, we segment the tumor from T1-weighted contrast enhancement modality T1ce. We used T1, T1c, T2 and flair images of 60 subject suffering from high graded and low grade glioma, and 20 T1-weighted anatomical models of normal brains.