dc.creator | Goudas, T. | en |
dc.creator | Maglogiannis, I. | en |
dc.date.accessioned | 2015-11-23T10:28:43Z | |
dc.date.available | 2015-11-23T10:28:43Z | |
dc.date.issued | 2012 | |
dc.identifier | 10.1109/EMBC.2012.6346946 | |
dc.identifier.isbn | 9781424441198 | |
dc.identifier.issn | 1557170X | |
dc.identifier.uri | http://hdl.handle.net/11615/28079 | |
dc.description.abstract | This paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images utilizing adaptive thresholding and a Support Vector Machines classifier. The segmentation results are also enhanced through a Majority Voting and a Watershed technique. The proposed tool was evaluated by experts on breast cancer images and the reported results were accurate and reproducible. © 2012 IEEE. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84870800082&partnerID=40&md5=e8338a3edc4ce1a06f6c63911b8ea66f | |
dc.subject | Adaptive thresholding | en |
dc.subject | Apoptotic cells | en |
dc.subject | Breast Cancer | en |
dc.subject | Cancer cells | en |
dc.subject | Image analysis techniques | en |
dc.subject | Image analysis tools | en |
dc.subject | Majority voting | en |
dc.subject | Microscopy images | en |
dc.subject | Segmentation results | en |
dc.subject | Image analysis | en |
dc.subject | Image segmentation | en |
dc.subject | Medical imaging | en |
dc.subject | Diseases | en |
dc.title | Cancer cells detection and pathology quantification utilizing image analysis techniques | en |
dc.type | conferenceItem | en |