dc.contributor.author | Loizidou, Kosmia | en |
dc.contributor.author | Skouroumouni, Galateia | en |
dc.contributor.author | Nikolaou, Christos | en |
dc.contributor.author | Pitris, Costas | en |
dc.creator | Loizidou, Kosmia | en |
dc.creator | Skouroumouni, Galateia | en |
dc.creator | Nikolaou, Christos | en |
dc.creator | Pitris, Costas | en |
dc.date.accessioned | 2021-01-26T09:45:28Z | |
dc.date.available | 2021-01-26T09:45:28Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/63238 | |
dc.description.abstract | Breast cancer is one of the most deadly malignancies worldwide and the second leading cause of death in women. Mammography, i.e. the screening for breast cancer with x-ray imaging, has significantly improved the prognosis of patients diagnosed with the disease. The evaluation of mammograms requires a panel of radiologists, but even well trained experts can err in their assessment. For this reason, Computer-Aided Detection (CAD) systems are becoming more prevalent. In this paper, we introduce a novel approach for breast Micro-Calcification (MC) diagnosis using temporal sequences of digital mammograms. The goal is to increase the MC detection accuracy by subtracting prior images. A new dataset, with precise marking of MC locations, was created specifically for this study. The proposed approach began with temporal subtraction of mammograms, after demon-based registration, which effectively removed unchanged regions and MCs (17.3% reduction in the number of MCs). The second step was the classification of the MCs as benign or suspicious using the subtracted images. A set of diverse features were selected for the classification. Four different classifiers were tested with leave-one-patient-out cross-validation. For comparison, the MC classification was also performed, using single mammograms, without temporal subtraction. The average accuracy of the classification of the MCs as benign or suspicious was 91.3% without and 99.2% with temporal subtraction using Support Vector Machines (statistically significant p=0.026). These results show that temporal subtraction could be a valuable addition to CAD systems to assist radiologists in effectively detecting breast MCs. | en |
dc.source | 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI) | en |
dc.title | A new method for breast micro-calcification detection and characterization using digital temporal subtraction of mammogram pairs | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/BHI.2019.8834517 | |
dc.description.startingpage | 1 | |
dc.description.endingpage | 4 | |
dc.author.faculty | Πολυτεχνική Σχολή / Faculty of Engineering | |
dc.author.department | Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering | |
dc.type.uhtype | Conference Object | en |
dc.contributor.orcid | Pitris, Costas [0000-0002-5559-1050] | |
dc.contributor.orcid | Skouroumouni, Galateia [0000-0002-6056-5797] | |
dc.contributor.orcid | Loizidou, Kosmia [0000-0002-0810-4926] | |
dc.gnosis.orcid | 0000-0002-5559-1050 | |
dc.gnosis.orcid | 0000-0002-6056-5797 | |
dc.gnosis.orcid | 0000-0002-0810-4926 | |