Active Contours: A New Distribution Metric for Image Segmentation

We present a new distribution metric for image segmentation for active contours.

How can we characterize an image, and does the same characterization yield the same result? In this work, we study one possible characterization, distribution metrics. That is, we assume the desired region of interest has a different probability distribution from its corresponding background. Using this, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies “distance” for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.

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