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In this paper, a novel brain tissue-based method of feature selection is proposed for detecting the dementia related to Alzheimer’s disease (AD-dementia) in magnetic resonance imaging (MRI). We begin with the transformation of MRI into the L*a*b* color space to have a good effect on image contrast. With statistical measurements, a subsequent multivariable thresholding segmentation is performed in order to find a measurable detection region of grey matter (GM) voxel intensities in the L* histograms (DRGMVI-L*), where the scale model of MRI in the L* histograms provides an interface between the boundary GM volume and the group phenomena of interest quantitatively for detecting AD-dementia. Experiments validate the approach using extracted voxel-based features of tissue in the DRGMVI-L* on the real-time data of OASIS. The study reveals that (a) on average, the group of normal control (NC) exhibits a larger volume of GM voxel significantly than those two subject groups with AD-dementia (the group of AD with CDR = 2 and the group of mild cognitive impairment (MCI) with CDR = 0.5 or 1), with likelihood ratios of NC/MCl/AD at 1:0.8721:0.7587 and 1:0.9524:0.8412 in the anatomical transverse and sagittal sections, respectively; (b) the group of NC appears to have a signaling pulse of GM phenomenally, compared to the groups of AD and MCI with approximately uniform across or slightly concave in the DRGMVI-L*; this may prove to be unique biomarkers of disease in differentiating subjects with AD-dementia from the NC group, and may further develop to assist in identifying difficult cases of MCI. Additionally, in the cross validation, the results show that the proposed approach improves the performance with a hit rate of 0.826 as compared with published results in the literature in the detection of AD-dementia in MRI. (C) 2017 Elsevier B.V. All rights reserved.