Model-based region-of-interest selection in dynamic breast MRI.
Magnetic resonance imaging (MRI) is emerging as a powerful tool for the diagnosis of breast abnormalities. Dynamic analysis of the temporal pattern of contrast uptake has been applied in differential diagnosis of benign and malignant lesions to improve specificity. Selecting a region of interest (ROI) is an almost universal step in the process of examining the contrast uptake characteristics of a breast lesion. We propose an ROI selection method that combines model-based clustering of the pixels with Bayesian morphology, a new statistical image segmentation method. We then investigate tools for subsequent analysis of signal intensity time course data in the selected region. Results on a database of 19 patients indicate that the method provides informative segmentations and good detection rates.
Forbes
Florence
F
équipe mistis, Inria Rhône-Alpes, Zirst, 655 av. de l'Europe, Montbonnot, 38334 Saint Ismier Cedex, France.
Peyrard
Nathalie
N
Fraley
Chris
C
Georgian-Smith
Dianne
D
Goldhaber
David M
DM
Raftery
Adrian E
AE
D001499:Bayes Theorem; D001941:Breast Diseases / Q000473:pathology*; D001943:Breast Neoplasms / Q000473:pathology; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*