Efficient and robust segmentations based on eikonal and diffusion PDEs

Authors:
Bertrand Peny;Gozde Unal;Greg Slabaugh;Tong Fang;Christopher Alvino
Affiliations:
Intelligent Vision and Reasoning, Siemens Corporate Research, Princeton, NJ;Intelligent Vision and Reasoning, Siemens Corporate Research, Princeton, NJ;Intelligent Vision and Reasoning, Siemens Corporate Research, Princeton, NJ;Intelligent Vision and Reasoning, Siemens Corporate Research, Princeton, NJ;Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Year:
2006

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Abstract

In this paper, we present efficient and simple image segmentations based on the solution of two separate Eikonal equations, each originating from a different region. Distance functions from the interior and exterior regions are computed, and final segmentation labels are determined by a competition criterion between the distance functions. We also consider applying a diffusion partial differential equation (PDE) based method to propagate information in a manner inspired by the information propagation feature of the Eikonal equation. Experimental results are presented in a particular medical image segmentation application, and demonstrate the proposed methods.