000001734 001__ 1734
000001734 005__ 20181207220028.0
000001734 037__ $$aCONFERENCE
000001734 041__ $$aeng
000001734 245__ $$aMedical x-ray image classification and retrieval system
000001734 260__ $$aChiayi, Taiwan$$b27 June- 1 July 2016$$c2016
000001734 269__ $$a2016-06
000001734 300__ $$a10 p.
000001734 506__ $$avisible
000001734 520__ $$9eng$$aMedical image retrieval systems have gained high interest in the scientific community due to the advances in medical imaging technologies. The semantic gap is one of the biggest challenges in retrieval from large medical databases. This paper presents a retrieval system that aims at addressing this challenge by learning the main concept of every image in the medical database. The proposed system contains two modules: a classification/annotation and a retrieval module. The first module aims at classifying and subsequently annotating all medical images automatically. SIFT (Scale Invariant Feature Transform) and LBP (Local Binary Patterns) are two descriptors used in this process. Imagebased and patch-based features are used as approaches to build a bag of words (BoW) using these descriptors. The impact on the classification performance is also evaluated. The results show that the classification accuracy obtained incorporating image-based integration techniques is higher than the accuracy obtained by other techniques. The retrieval module enables the search based on text, visual and multimodal queries. The text-based query supports retrieval of medical images based on categories, as it is carried out via the category that the images were annotated with, within the classification module. The multimodal query applies a late fusion technique on the retrieval results obtained from text-based and image-based queries. This fusion is used to enhance the retrieval performance by incorporating the advantages of both text-based and content-based image retrieval.
000001734 592__ $$aHEG - Valais
000001734 592__ $$bInstitut Informatique de gestion
000001734 592__ $$cEconomie et Services
000001734 65017 $$aInformatique
000001734 6531_ $$9eng$$amedical x-ray images
000001734 6531_ $$9eng$$abag of visual words
000001734 6531_ $$9eng$$amedical image classification
000001734 6531_ $$9eng$$amedical image annotation
000001734 6531_ $$9eng$$amedical image retrieval
000001734 655_7 $$afull paper
000001734 700__ $$aZare, Mohammad Reza$$uSchool of Information technology, Monash University, Malaysia
000001734 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000001734 711__ $$aPacific Asia Conference on Information Systems (PACIS) 2016$$cChiayi, Taiwan$$d27/06/2016 / 01/07/2016
000001734 773__ $$tProceedings of the Pacific Asia Conference on Information Systems (PACIS) 2016
000001734 8564_ $$s367043$$uhttp://hesso.tind.io/record/1734/files/muller_medicalxrayimage_2016.pdf
000001734 906__ $$aNONE
000001734 909CO $$ooai:hesso.tind.io:1734$$pDoc_type_Conferences$$pHEG_ALL$$pGLOBAL_SET
000001734 950__ $$aI1
000001734 980__ $$aconference