Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching

Authors:
Gökhan Ertaş;H.Özcan Gülçür;Onur Osman;Osman N. Uçan;Mehtap Tunacı;Memduh Dursun
Affiliations:
Biomedical Engineering Institute, Boaziçi University, Bebek 34342, Istanbul, Turkey;Biomedical Engineering Institute, Boaziçi University, Bebek 34342, Istanbul, Turkey;Istanbul Commerce University, Eminonu 34378, Istanbul, Turkey;Electrical and Electronics Engineering Department, Engineering Faculty, Istanbul University, Avcilar 34850, Istanbul, Turkey;Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Capa 34390, Istanbul, Turkey;Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Capa 34390, Istanbul, Turkey
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

A 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 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap 0.85 and misclassification rate