title: [An annotation approach for masto-calcifications based on semantic model].
abstract: To realize the medical semantic annotation of mammogram, a semantic modeling approach for micro-calcifications in mammogram based on hierarchical Bayesian network (BN) was proposed. Firstly, support vector machines (SVM) were used to map low-level image feature into feature semantics, then high-level semantic was captured through fusing the feature semantics using BN. Finally semantic model was established. To validate the method, the model was applied to annotate the semantic information of mammograms. In this experiment, 142 images were chosen as training set and 50 images as testing set. The results showed that the accuracy of malignant samples was 81.48%, and that of benign samples was 73.91%.
authors.affiliation: College of Electrical and Electronic Engineering, Harbin Univeristy of Science Technology, Harbin 150080, China.
authors.forename: Kexin
authors.initials: K
authors.lastname: Zhao
authors.forename: Lixin
authors.initials: L
authors.lastname: Song
mesh_terms: D001499:Bayes Theorem; D001941:Breast Diseases / Q000000981:diagnostic imaging / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008962:Models, Theoretical; D011859:Radiography; D012660:Semantics; D060388:Support Vector Machine