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.
Highlights
The detection of white matter hyperintensity (WMH) by magnetic resonance imaging (MRI) can be used to diagnoseThe associate editor coordinating the review of this article and approving it for publication was Sunil Karamchandani.stroke, cognitive impairment, dementia, and even the risk of death as pointed out in the past literature [1], [2]
The experimental results show that when synthetic MR images were used for experiments there was no significant difference among iterative linearly constrained minimum variance (ILCMV) and Multiclass ICEM (MCICEM) and MCICEM-4DSI in classification of the normal brain tissue and WMH
Since accurate and simultaneous classification of brain normal tissue and WMH are very important for assessing the cognitive deficits of dementia patients and the brain tissue changes of the elderly, this paper verifies the accuracy and feasibility of the proposed ILCMV via synthetic brain MR image and real brain MR image experiments
Summary
The detection of white matter hyperintensity (WMH) by magnetic resonance imaging (MRI) can be used to diagnoseThe associate editor coordinating the review of this article and approving it for publication was Sunil Karamchandani.stroke, cognitive impairment, dementia, and even the risk of death as pointed out in the past literature [1], [2]. Clinicians make a diagnosis through visual inspection and quantify WMH by manual selection. Some works reported in the literature pointed out that the total volume of WMH in subcortical area was related to the rate of decline in cognitive and memory [4]. If the volume of WMH can be accurately detected and quantified, it will have substantial significance in assisting clinical diagnosis. Since it is too cumbersome and not feasible in practical diagnosis to depict the WMH area manually, developing computer aided tools can reduce the complexity of the diagnosis and help clinicians have better diagnosis and monitoring
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