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

Yaniv Gal ; Andrew Mehnert (Institutionen för 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) p. 124-129. (2007)
[Konferensbidrag, refereegranskat]

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.

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Denna post skapades 2013-06-18.
CPL Pubid: 178728


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Institutioner (Chalmers)

Institutionen för signaler och system (1900-2017)


Medicinsk bildbehandling

Chalmers infrastruktur