Jamilson Bispo dos Santos, Jorge Rady de Almeida, Leandro Augusto Silva
{"title":"Pattern recognition in mammographic images used by the residents in mammography","authors":"Jamilson Bispo dos Santos, Jorge Rady de Almeida, Leandro Augusto Silva","doi":"10.1109/ICCMA.2013.6506175","DOIUrl":null,"url":null,"abstract":"This paper presents a computational strategy for content based image retrieval (CBIR-Content-Based Image Retrieval), considering the similarity in relation to an image already selected. The identification of similarity is obtained by feature extraction, using the technique of wavelet combined with Hu moments. The classification of mammographic is performed using Artificial Neural Networks, through the classifier Self-Organizing Map (SOM). The proposed method is tested with a database of the Laboratory of Medical Image Classification (QUALIM) Department of Diagnostic Imaging, Federal University of São Paulo (UNIFESP).","PeriodicalId":187834,"journal":{"name":"2013 International Conference on Computer Medical Applications (ICCMA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Computer Medical Applications (ICCMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCMA.2013.6506175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a computational strategy for content based image retrieval (CBIR-Content-Based Image Retrieval), considering the similarity in relation to an image already selected. The identification of similarity is obtained by feature extraction, using the technique of wavelet combined with Hu moments. The classification of mammographic is performed using Artificial Neural Networks, through the classifier Self-Organizing Map (SOM). The proposed method is tested with a database of the Laboratory of Medical Image Classification (QUALIM) Department of Diagnostic Imaging, Federal University of São Paulo (UNIFESP).