Ertaş, GökhanGülçür, H.ÖzcanOsman, OnurUçan, Osman N.Tunacı, MehtapDursun, Memduh2020-11-212020-11-2120080010-4825https://doi.org/10.1016/j.compbiomed.2007.08.001https://hdl.handle.net/11467/3499PubMed ID: 17854795A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12 × 12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap > 0.85 and misclassification rate < 0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344 slices × 6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity = 100 %), however, there were some false-positive detections (31%/lesion, 10%/slice). © 2007 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccess3D template matchingCellular neural networkLesion detectionMR mammographySegmentationArticle381116126Q2WOS:000252918000013Q12-s2.0-370490355891785479510.1016/j.compbiomed.2007.08.001