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

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Ertas G., Guelcuer H. O., Osman O., Ucan O. N., Tunaci M., Dursun M.

COMPUTERS IN BIOLOGY AND MEDICINE, vol.38, no.1, pp.116-126, 2008 (SCI-Expanded, Scopus)

• Publication Type: Article / Article
• Volume: 38 Issue: 1
• Publication Date: 2008
• Doi Number: 10.1016/j.compbiomed.2007.08.001
• Journal Name: COMPUTERS IN BIOLOGY AND MEDICINE
• Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
• Page Numbers: pp.116-126
• Istanbul University Affiliated: Yes

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 12 x 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.