Model-Based Region-of-Interest Selection in Dynamic Breast MRI.

摘要

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 modelbased 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 data base of 19 patients are promising. The method provides informative segmentations and good detection rates are obtained.