title: A generative model based approach to retrieving ischemic stroke images.
abstract: This paper proposes a generative model approach to automatically annotate medical images to improve the efficiency and effectiveness of image retrieval systems for teaching, research, and diagnosis. The generative model captures the probabilistic relationships among relevant classification tags, tentative lesion patterns, and selected input features. Operating on the imperfect segmentation results of input images, the probabilistic framework can effectively handle the inherent uncertainties in the images and insufficient information in the training data. Preliminary assessment in the ischemic stroke subtype classification shows that the proposed system is capable of generating the relevant tags for ischemic stroke brain images. The main benefit of this approach is its scalability; the method can be applied in large image databases as it requires only minimal manual labeling of the training data and does not demand high-precision segmentation of the images.
authors.affiliation: National University of Singapore, Singapore.
authors.forename: Thien Anh
authors.initials: TA
authors.lastname: Dinh
authors.forename: Tomi
authors.initials: T
authors.lastname: Silander
authors.forename: C C Tchoyoson
authors.initials: CC
authors.lastname: Lim
authors.forename: Tze-Yun
authors.initials: TY
authors.lastname: Leong
mesh_terms: D001499:Bayes Theorem; D002545:Brain Ischemia / Q000175:diagnosis*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D020521:Stroke / Q000175:diagnosis*