A segmentation algorithm for contrast-enhanced images

Medical imaging often involves the injection of contrast agents and the subsequent analysis of tissue enhancement patterns. Many important types of tissue have characteristic enhancement patterns; for example, in magnetic resonance (MR) mammography, malignancies exhibit a characteristic "wash out" temporal pattern, while in MR angiography, arteries, veins and parenchyma each have their own distinctive temporal signature. In such image sequences, there are substantial changes in intensities; however, this change is due primarily to the contrast agent rather than the motion of scene elements. As a result, the task of segmenting contrast-enhanced images poses interesting new challenges for computer vision. We propose a new image segmentation algorithm for image sequences with contrast enhancement, using a model-based time series analysis of individual pixels. We use energy minimization via graph cuts to efficiently ensure spatial coherence. The energy is minimized in an expectation-maximization fashion that alternates between segmenting the image into a number of nonoverlapping regions and finding the temporal profile parameters which best describe the behavior of each region. Preliminary experiments on MR mammography and MR angiography studies show the algorithm\´s ability to find an accurate segmentation.