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Breast dynamic contrast enhanced MRI (DCE-MRI) segmentation, based on the differential enhancement of image intensities, can help the clinician detect suspicious regions. Motivated by the recent success of texture learning and segmentation, we propose a novel segmentation method based on texture properties. The segmentation method consists of generating a library of texture primitives "textons", and then classifying each voxel into different tissue classes using textons and vector attributes. A Markov Random Measure field (MRF) method is combined with texture information to realise the spatial coherence. To evaluate our framework, twenty patients' MRIs from our local hospital were used for texture learning, and a further twenty patients' MRIs were used for testing. © 2008 Springer-Verlag Berlin Heidelberg.

Original publication




Conference paper

Publication Date



5116 LNCS


689 - 695