Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images

• Published in: Magnetic resonance imaging Vol. 62; pp. 167 - 173
• Main Authors: Shaikh, Tawseef Ayoub, Ali, Rashid
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.10.2019
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Atrophy - diagnostic imaging; Atrophy - pathology; Brain - diagnostic imaging; Brain - pathology; Brain Mapping; Classifier; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Computer-assisted diagnosis (CAD); DWT (Discrete Wavelet Transform); False Positive Reactions; Female; Humans; Image Processing, Computer-Assisted - methods; Least-Squares Analysis; Machine intelligence; Magnetic Resonance Imaging; Male; Middle Aged; Pattern Recognition, Automated; Principal Component Analysis; ROC Curve; Support Vector Machine; Support Vector Machine (SVM); Wavelet Analysis; DWT (Discrete Wavelet Transform); Computer-assisted diagnosis (CAD); Classifier; Support Vector Machine (SVM); Alzheimer's disease; Machine intelligence
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2019.06.019

An inventive scheme for automated tissue segmentation and classification is offered in this paper using Fast Discrete Wavelet Transform (DWT)/Band Expansion Process (BEP), Kernel-based least squares Support Vector Machine (KLS-SVM) and F-score, backed by Principal Component Analysis (PCA). Using input as T1, T2 and Proton Density (PD) scans of patients, CSF (Cerebrospinal Fluid), WM (White matter) and GM (Gray matter) are afforded as output, which act as hallmark for brain atrophy and thus sustaining in diagnosis of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) and Healthy Controls (HC). The blending of BEP features from DWT and texture features from Gray Level Co-occurrence Matrix (GLC) promises to be a savior in atrophy revelation of the segmented tissues. Data used for evaluation of this study is taken from the ADNI database that encloses T1-weighted s-MRI (Structural Magnetic Imaging Resonance) scans of 158 patients with AD and 145 HC. Preprocessing steps unearthed five parameters for classification (i.e. cortical thickness, curvature, gray matter volume, surface area, and sulcal depth), in the preliminary step. For challenging the classifier performance, ROC (Receiver operating characteristics) curves are painted and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The final results revealed that Fast DWT + F-Score + PCA + KLS-SVM + Poly Kernel is giving 100% tissue classification accuracy for test samples under consideration with only 7 input features.