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

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

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.38, sa.1, ss.116-126, 2008 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 1
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.compbiomed.2007.08.001
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.116-126
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.