title: Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.
abstract: OBJECTIVE
  This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation.
  
  
  METHODS
  The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary.
  
  
  RESULTS
  The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy.
  
  
  CONCLUSIONS
  In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease.
authors.affiliation: School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstr. 9A, 30167 Hannover, Germany. Electronic address: shaikhli@tnt.uni-hannover.de.
authors.forename: Saif Dawood Salman
authors.initials: SD
authors.lastname: Al-Shaikhli
authors.affiliation: ITC - Faculty of Geo-Information Science and Earth Observation, Department of Earth Observation Science, University of Twente, Netherlands.
authors.forename: Michael Ying
authors.initials: MY
authors.lastname: Yang
authors.affiliation: Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstr. 9A, 30167 Hannover, Germany.
authors.forename: Bodo
authors.initials: B
authors.lastname: Rosenhahn
mesh_terms: D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis*; D001331:Automation; D002421:Caudate Nucleus / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional; D007858:Learning
keywords: 3D segmentation; Alzheimer; Caudate nucleus; Dictionary learning; MRI-T1 medical image