The aim of this article is to study the texture features of cingulum in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on magnetic resonance images, and explore the texture differences derived from different gender among each group. Texture analysis was performed on 7 AD patients, 14 MCI patients and 11 normal controls (NC). Texture features extracted from gray level co-occurrence matrix and run-length matrix were analyzed between each two groups. The results showed that texture features of the anterior cingulum had significant differences in the multiple comparisons and features of the posterior cingulum had significant differences between AD and MCI group as well as AD and NC group. There were significant differences between AD and MCI group as well as AD and NC group in male’s cingulum. While in female’s cingulaum, the differences were founded between AD and NC group. The results indicated that the pathological changes in cingulum could be reflected by texture features and the pathological changes may be different in the two genders.
Voxel-based morphometry method (VBM) has been widely applied to detect the brain atrophy and achieved promising results; however, the effect of the segmentation step in VBM is not clear and the new segmentation method in SPM8 hasn’t been used in Alzheimer’s disease (AD) studies. The aim of this study is to investigate the locations and degrees of grey matter (GM), white matter (WM) atrophy and evaluate the results derived from two segmentation methods. Magnetic resonance imaging (MRI) was collected in 16 AD patients and 16 healthy controls (HC). Using two segmentation methods respectively, several reduction clusters of GM and WM were detected but the locations and degrees of reduction volumes were discrepant resulted from different segmentation methods. Our results suggest that VBM is an effective tool to analyze AD brain atrophy and based on VBM, the comparison of the locations and degrees of volume reduction among AD researches through different segmentation methods should be cautious.
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