title: Current methods in medical image segmentation.
abstract: Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Laboratory of Personality and Cognition, National Institute on Aging, Baltimore, Maryland, USA. pham@jhu.edu
authors.forename: D L
authors.initials: DL
authors.lastname: Pham
authors.forename: C
authors.initials: C
authors.lastname: Xu
authors.forename: J L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D001921:Brain / Q000033:anatomy & histology; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D006321:Heart / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging; D008327:Mammography; D008390:Markov Chains; D008953:Models, Anatomic; D016571:Neural Networks, Computer