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11 March 2008A new distribution metric for image segmentation
In this paper, 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. Moreover, we briefly provide a
theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance
originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are
directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance
and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. 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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Romeil Sandhu, Tryphon Georgiou, Allen Tannenbaum, "A new distribution metric for image segmentation," Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691404 (11 March 2008); https://doi.org/10.1117/12.769010