An Iterative Mixed Pixel Classification for Brain Tissues and White Matter Hyperintensity in Magnetic Resonance Imaging

Abstract

White matter hyperintensities (WMH) generally can be detected and diagnosed by magnetic resonance imaging (MRI). It has been pointed out that WMH is closely associated with stroke, cognitive impairment, dementia, and even is very relevant to the increased risk of death. This paper proposes a new iterative linearly constrained minimum variance (ILCMV) classification-based method which expands an iterative constrained energy minimization (ICEM) detection-based method developed for hyperspectral image classification. It explores the potential of ILCMV combined with different spatial filters in classification of brain normal tissues and WMH and also develops an alternative version of ILCMV, called Multi-class ICEM (MCICEM) for a comparative study. The synthetic images in BrainWeb are used for quantitative evaluation of ILCMV and the real brain MR images are used for visual assessment. The experimental results suggest that the Gaussian filter is most suitable for ILCMV and MCICEM if the computational time is factored into consideration. Otherwise, ILCMV/MCICEM combined with a Gabor filter yields the best classification. In addition, the average Dice similarity indexes (DSI) of CSF/GM/WM volume measurement produced by ILCMV method combined with Gaussian filter were 0.936/0.948/0.975 in synthetic MR images with all noise levels and were better than the results reported in the literature. ILCMV can simultaneously classifies brain normal tissues and WMH lesions in MR brain images and does better than detection of WMH alone. In addition, its computational time is also less than MCICEM. It is our belief that the proposed methodology demonstrates its promising in classification of brain tissue and WMH in MRI applications.

Keywords

• Iterative linear constrained minimum variance (ILCMV)
• White matter hyperintensities (WMH)
• magnetic resonance imaging (MRI)
• brain tissue classification