WMH Detection Using Improved AIR-AHE-Based Algorithm for Two-Tier Segmentation Technique

The segmentation of magnetic resonance imaging (MRI) brain images could be implemented using any technique, either automatic or manual. The different methods commonly show different results because their performance relies on the segmentation precision and accuracy. In this paper, a new image segmentation algorithm is proposed based on k-means and AIR-AHE clustering algorithm to automatically segment and classify WMH severity in brain white matter region. The objective of this new segmentation algorithm is to minimize the false positive (FP) in white matter hyper-intensity (WMH) detection and hence will increase the WMH detection accuracy in MRI images. The proposed algorithm is implemented on two-tier segmentation system by identifying the edge of WMH and WM boundary for image mapping purpose. T2-weighed imaging (T2-WI) and fluid-attenuated inversion recovery (FLAIR) MRI sequences are used for mapping most precise WMH region of interest (ROI). From the experimental results, the proposed algorithm produces significant improvement in terms of correct WMH localization and reduces the false WMH detection. Based on the accuracy and capabilities of the proposed algorithm, this algorithm is suitable to be implemented to aid radiologist in the image analysing.