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Common Statistical Approach
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
As represented by VBM, a method that applies a GLM for each voxel and displays statistics calculated from the estimated parameters is often used. The objective variable is the voxel value in the voxel k, an n number of subjects are assumed, and the objective variable vector is defined as Yk = (Yk1, Yk2, …, Ykn)T. k = 1, 2, …, V, and V is the number of voxels. On the other hand, consider the following model.
Artifacts and Pitfalls in Diffusion MRI
Published in Ioannis Tsougos, Advanced MR Neuroimaging, 2018
VBM was originally designed to measure changes of gray matter on structural T1-weighted data; nevertheless it is becoming more and more popular as a method for analyzing quantitative images since it is quite automated with minimal intervention from the user.
Leung-Malik Features and Adaboost Perform Classification of Alzheimer’s Disease Stages
Published in IETE Journal of Research, 2022
Shaik Basheera, M. Satya Sai Ram
Alzheimer’s is a progressive neurodegenerative disorder where the neurons get loss and it affects patients’ mental condition [2]. Alzheimer’s disease is thought to have infected 20 years from the date of diagnosis and small changes are observed in the initial days of the disease [3]. Many researchers created CAD systems employing machine learning techniques to identify structural and functional brain changes for accurate and reliable AD Stage categorization. Some of the CAD systems worked on AD classification are reviewed below [4]. Region-based changes in morphology may be occurring in several anatomical areas of the brain, and these changes may be experienced differently by those with Alzheimer’s disease, MCI, and CN. In voxel-based morphometry, also known as VBM, the complete brain is analyzed so that changes in the cortex can be measured and the thickness of the cortex can be calculated [5]. When classifying AD, VBM, and texture features created by GLCM and Gabor Filter are used as the primary tools [6]. They created the Hippocampus volume Integrity method to automatically estimate the hippocampal volume and distinguish between AD, MIC, and CN. The stages of Alzheimer’s disease may be estimated using subcortical areas such as the GM, WM, and CSF [7]. One drawback of VBM computation is that it cannot identify changes in subcortical areas [8]. The change in WM is assessed in order to analyze the region of the brain affected by Alzheimer’s disease [9]. The regional thicknesses of the hippocampal, ventricular, and GM of the brain’s subcortical regions are used in the process of analyzing neuroimaging [10]. The Region of Interest is an automated approach that may diagnose Alzheimer’s disease and the development of mild cognitive impairment [11]. In order to conduct the classification, many morphological characteristics are combined, and a random forest consisting of 2000 trees is used.