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

Yaniv Gal, Andrew Mehnert, Andrew Bradley, Kerry McMahon, Stuart Crozier

Research output: Chapter in Book/Conference paperConference paperpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - Digital Image Computing Techniques and Applications
Subtitle of host publication9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007
Number of pages6
Publication statusPublished - 1 Dec 2007
Externally publishedYes
EventAustralian Pattern Recognition Society (APRS) - Glenelg, SA, Australia
Duration: 3 Dec 20075 Dec 2007


ConferenceAustralian Pattern Recognition Society (APRS)
CityGlenelg, SA


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