1. Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching
- Author
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H. Ozcan Gulcur, Mehtap Tunaci, Gokhan Ertas, Onur Osman, Memduh Dursun, Osman N. Ucan, and Bölüm Yok
- Subjects
Adult ,medicine.medical_specialty ,MR mammography ,Breast Neoplasms ,Lesion detection ,Health Informatics ,Sensitivity and Specificity ,Lesion ,Imaging, Three-Dimensional ,Segmentation ,Software Design ,Cellular neural network ,Image Interpretation, Computer-Assisted ,Humans ,Medicine ,False Positive Reactions ,Aged ,Aged, 80 and over ,business.industry ,Template matching ,Middle Aged ,Image Enhancement ,Magnetic Resonance Imaging ,Computer Science Applications ,Magnetic resonance mammography ,Detection performance ,Female ,Neural Networks, Computer ,Radiology ,medicine.symptom ,Mr images ,business ,Nuclear medicine ,Algorithms ,3D template matching - Abstract
PubMed ID: 17854795 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 × 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.
- Published
- 2008