title: A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis.
abstract: OBJECTIVE
  The cause of the Alzheimer's disease is poorly understood and to date no treatment to stop or reverse its progression has been discovered. In developed countries, the Alzheimer's disease is one of the most financially costly diseases due to the requirement of continuous treatments as well as the need of assistance or supervision with the most cognitively demanding activities as time goes by. The objective of this work is to present an automated approach for classifying the Alzheimer's disease from magnetic resonance imaging (MRI) patient brain scans. The method is fast and reliable for a suitable and straightforward deploy in clinical applications for helping diagnosing and improving the efficacy of medical treatments by recognising the disease state of the patient.
  
  
  METHODS
  Many features can be extracted from magnetic resonance images, but most are not suitable for the classification task. Therefore, we propose a new feature extraction technique from patients' MRI brain scans that is based on a recent computer vision method, called Oriented FAST and Rotated BRIEF. The extracted features are processed with the definition and the combination of two new metrics, i.e., their spatial position and their distribution around the patient's brain, and given as input to a function-based classifier (i.e., Support Vector Machines).
  
  
  RESULTS
  We report the comparison with recent state-of-the-art approaches on two established medical data sets (ADNI and OASIS). In the case of binary classification (case vs control), our proposed approach outperforms most state-of-the-art techniques, while having comparable results with the others. Specifically, we obtain 100% (97%) of accuracy, 100% (97%) sensitivity and 99% (93%) specificity for the ADNI (OASIS) data set. When dealing with three or four classes (i.e., classification of all subjects) our method is the only one that reaches remarkable performance in terms of classification accuracy, sensitivity and specificity, outperforming the state-of-the-art approaches. In particular, in the ADNI data set we obtain a classification accuracy, sensitivity and specificity of 99% while in the OASIS data set a classification accuracy and sensitivity of 77% and specificity of 79% when dealing with four classes.
  
  
  CONCLUSIONS
  By providing a quantitative comparison on the two established data sets with many state-of-the-art techniques, we demonstrated the effectiveness of our proposed approach in classifying the Alzheimer's disease from MRI patient brain scans.
authors.affiliation: Institute of Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Turini 19, 00185 Rome, Italy; Uninettuno International University, Department of Engineering, Corso Vittorio Emanuele II 39, 00186 Rome, Italy. Electronic address: previtali@dis.uniroma1.it.
authors.forename: F
authors.initials: F
authors.lastname: Previtali
authors.affiliation: Institute of Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Turini 19, 00185 Rome, Italy.
authors.forename: P
authors.initials: P
authors.lastname: Bertolazzi
authors.affiliation: Institute of Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Turini 19, 00185 Rome, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Felici
authors.affiliation: Institute of Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Turini 19, 00185 Rome, Italy.
authors.forename: E
authors.initials: E
authors.lastname: Weitschek
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging; D018450:Disease Progression; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D060388:Support Vector Machine
keywords: Alzheimer’s disease; Classification; Feature extraction; Magnetic resonance imaging