C. Marrocco, M. Molinara, F. Tortorella, P. Rinaldi, L. Bonomo, Alfredo Ferrarotti, C. Aragno, Salvatore Schiano lo Moriello
{"title":"Detection of cluster of microcalcifications based on watershed segmentation algorithm","authors":"C. Marrocco, M. Molinara, F. Tortorella, P. Rinaldi, L. Bonomo, Alfredo Ferrarotti, C. Aragno, Salvatore Schiano lo Moriello","doi":"10.1109/CBMS.2012.6266365","DOIUrl":null,"url":null,"abstract":"The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2012.6266365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.