A multi‐modal, multi‐atlas‐based approach for Alzheimer detection via machine learning

• Published in: International journal of imaging systems and technology 2018-06, Vol.28 (2), p.113-123
• Main Authors: Asim, Yousra, Raza, Basit, Malik, Ahmad Kamran, Rathore, Saima, Hussain, Lal, Iftikhar, Mohammad Aksam
• Format: Article
• Language: English
• Subjects: AAL atlas; Alzheimer disease; Alzheimer's disease; Biomarkers; Brain; Classification; Feature extraction; LPBA40 atlas; Machine learning; Magnetic resonance imaging; Mathematical analysis; Medical imaging; mild cognitive impairment; NMR; Nuclear magnetic resonance; Positron emission; Principal components analysis; Support vector machines; SVM classification; Tomography
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22263

The use of biomarkers for early detection of Alzheimer's disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.