P. Delogu, M. Fantacci, Alessandro Preite Martinez, A. Retico, A. Stefanini, Alessandro Tata
{"title":"A scalable system for microcalcification cluster automated detection in a distributed mammographic database","authors":"P. Delogu, M. Fantacci, Alessandro Preite Martinez, A. Retico, A. Stefanini, Alessandro Tata","doi":"10.1109/NSSMIC.2005.1596609","DOIUrl":null,"url":null,"abstract":"A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different datasets of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis","PeriodicalId":105619,"journal":{"name":"IEEE Nuclear Science Symposium Conference Record, 2005","volume":"119 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nuclear Science Symposium Conference Record, 2005","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2005.1596609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different datasets of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis
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