Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images

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.

Mehnert, Andrew,1967Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)mehnert (author)
Bradley, Andrew (author)
McMahon, Kerry (author)
Crozier, Stuart (author)
Chalmers tekniska högskola (creator_code:org_t)

In:Proc. 2007 Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA), s. 124-1290769530672

By the author/editor: Gal, Yaniv; Mehnert, Andrew, ...; Bradley, Andrew; McMahon, Kerry; Crozier, Stuart

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Medical Engineer ...; and Medical Image Pr ...

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