Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images
Paper i proceeding, 2007

We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.


Yaniv Gal

Andrew Mehnert

Chalmers, Signaler och system

Andrew Bradley

Kerry McMahon

Stuart Crozier

Proc. 2007 Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA)



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