Abstract
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.

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