Abstract
White matter hyperintensities (WMHs) are closely related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders of elderly people, and can be generally detected on T2 weighted (T2W) or fluid attenuation inversion recovery (FLAIR) brain magnetic resonance (MR) images. This paper develops a new approach to detect WMH in MR brain images from a hyperspectral imaging perspective. To take advantage of hyperspectral imaging, a nonlinear band expansion (NBE) process is proposed to expand MR images to a hyperspectral image. It then redesigns the well-known hyperspectral subpixel target detection, called constrained energy minimization (CEM), as an iterative version of CEM (ICEM) for WMH detection. Its idea is to implement CEM iteratively by feeding back Gaussian filtered CEM-detection maps to capture spatial information. To show effectiveness of NBE-ICEM in WMH detection, the lesion segmentation tool (LST), which is an open source toolbox for statistical parametric mapping (SPM), is used for comparative study. For quantitative analysis, the synthetic images in BrainWeb provided by McGill University are used for experiments where our proposed NBE-ICEM performs better than LST in all cases, especially for noisy MR images. As for real images collected by Taichung Veterans General Hospital, the NBE-ICEM also shows its advantages over and superiority to LST.
Highlights
White matter hyperintensities (WMHs) are commonly observed on T2 weighted (T2W) or fluid attenuation inversion recovery (FLAIR) brain magnetic resonance (MR) images of elderly people and related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders [1]
This paper presents a new application of hyperspectral imaging in WMH detection of MR brain images
One is an introduction of nonlinear band expansion (NBE) into constrained energy minimization (CEM), which expands the original MR images by including nonlinearly correlated band images generated by NBE to make a multispectral MR image into a hyperspectral MR image since CEM is a hyperspectral imaging technique
Summary
White matter hyperintensities (WMHs) are commonly observed on T2W or FLAIR brain MR images of elderly people and related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders [1]. Segmentation of WMH has been recently directed to semi-unsupervised and automatic methods which rely on computer assisted tools to help diagnosis to avoid human subjective interpretation. Most importantly, such computer assisted diagnosis can be further used to quantify WMH and calculate its volume [3,4,5,6,7]. The other is selection of an appropriate threshold, which determines the detection results of WMH Such automatic method is not fully automatic but rather semi-unsupervised because it requires adaptively adjusting threshold values by visual inspection. This paper takes a quite different approach to designing a joint spectral–spatial method that takes advantage of spectral properties provided by MR image sequences to perform subvoxel detection in conjunction with a Gaussian spatial filter to capture spatial contextual information surrounding the WMH detected voxels
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