White Matter Hyperintensities Extraction Based T2-FLAIR MRI Using Non-Local Means Filter and Nearest Neighbor Algorithm

Brain White matter appearing as hyperintensities on T2-FLAIR Magnetic Resonance Imaging (MRI) has the association with risk of stroke or dementia such as Alzheimer´s diseases and vascular dementia. In many researches, WMH are also demonstrated that they can predict an increased risk of cerebrovascular diseases. WMH are counted as an intermediate marker to identify a new risk factor based on their quantitative measurement. In this paper, we propose a method to extract WMH areas from T2- FLAIR MRI in order to measure WMH automatically. The proposed method consists of two segmentation steps. In the first phase, the combining of k-means clustering with morphology techniques is applied to remove brain matter out of cranium from T2- FLAIR MRI input image. Then in the second segmentation phase, non-local means filter is applied to the extracted brain for image denoising and nearest neighbor algorithm is used to separate brain image into 3 different classes of WMH area, non-WMH area and background area. The collected information is extremely essential in monitoring disease progress. Therefore, it plays an important role in developing computer-aided diagnosis (CAD) systems for detecting many cerebrovascular diseases.