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 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 time resolved image series, there are
substantial changes in intensities; however, this change is due primarily to
the contrast agent, rather than to motion. As a result, the task of
automatically segmenting contrast-enhanced images poses interesting new
challenges. In this paper, we propose a new image segmentation algorithm for
time resolved image series with contrast enhancement, using a model-based time
series analysis of individual pixels. We take an energy minimization approach
to ensure spatial coherence. The energy is minimized in an
expectation-maximization fashion that alternates between segmenting the image
into a number of non-overlapping regions and finding the temporal profile
parameters which describe the behavior of each region. Preliminary experiments
on MR angiography and MR mammography studies show the algorithm's ability to
find an accurate segmentation.
