A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis

• Published in: Computer methods and programs in biomedicine Vol. 143; pp. 89 - 95
• Main Authors: Previtali, F, Bertolazzi, P, Felici, G, Weitschek, E
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01-05-2017
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - classification; Alzheimer Disease - diagnostic imaging; Alzheimer’s disease; Brain - diagnostic imaging; Classification; Disease Progression; Feature extraction; Female; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Internal Medicine; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Models, Statistical; Other; Pattern Recognition, Automated; Predictive Value of Tests; Reproducibility of Results; Sensitivity and Specificity; Software; Support Vector Machine; Feature Extraction; Magnetic Resonance Imaging; Alzheimer’s Disease; Classification; Feature extraction; Alzheimer’s disease; Magnetic resonance imaging
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2017.03.006

Highlights • Supervised classification of Alzheimer disease patients • A novel technique for feature extraction from magnetic resonance images • Combination of key points spatial position and their distribution around the patients brain • Experimental evidence on real biomedical data sets • The method outperforms state-of-the-art approaches in terms of classification performance