title: Classification of brain disease in magnetic resonance images using two-stage local feature fusion.
abstract: BACKGROUND
  Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale.
  
  
  METHODS
  Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to.
  
  
  RESULTS
  We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block.
  
  
  CONCLUSIONS
  This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Li
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Wu
authors.initials: W
authors.lastname: Li
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Yehui
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Wensheng
authors.identifier: http://orcid.org/0000-0003-0752-941X
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000473:pathology; D001927:Brain Diseases / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D010300:Parkinson Disease / Q000473:pathology