title: Automatic medical image annotation and keyword-based image retrieval using relevance feedback.
abstract: This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.
authors.affiliation: Shindang-Dong Dalseo-Gu, Department Of Computer Engineering, Keimyung University, Daegu, 704-701, South Korea. niceko@kmu.ac.kr
authors.forename: Byoung Chul
authors.initials: BC
authors.lastname: Ko
authors.forename: JiHyeon
authors.initials: J
authors.lastname: Lee
authors.forename: Jae-Yeal
authors.initials: JY
authors.lastname: Nam
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D016208:Databases, Factual; D004282:Documentation / Q000379:methods; D005246:Feedback; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics