A resistive-network model for image segmentation

Peter J. Yim

doi:10.1117/12.768108

11 March 2008 A resistive-network model for image segmentation

Peter J. Yim

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 691403 (2008) https://doi.org/10.1117/12.768108
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

A novel physical analogy and associated algorithm is proposed for image segmentation. The physical analogy is that of an electrical network. The network is composed of discrete resistive elements that are interconnected, according to adjacency relations in the image. The resistances vary linearly with the image gradient and exponentially with the voltage across the elements. Segmentation is obtained by applying a voltage between a voltage source and ground that are located at points on the inside and outside of an object, respectively. Those points are identified in an interactive manner. Provided that a sufficiently high voltage is applied, a dichotomization of the voltages in the network occurs. In practice, the solution can be obtained in an iterative manner by gradually increasing the applied voltage. At each step as the source voltage is increased, the resistances are held constant and the Kirchoff's-law system of equations is solved algebraically. It is not clear yet whether image segmentation obtained by this algorithm is globally optimal with respect to a cost-function, as is the case for minimum s-t cut image segmentation. However, like minimum s-t cut algorithm, the resistive-network algorithm does tend to favor segmentation boundaries with shorter, and thus smoother, boundaries. A potential advantage of the resistive-network algorithm is that it is amenable to parallelization. The algorithm was found to provide reasonable results for segmentation of a synthetic image of step boundary degraded by blurring and Gaussian noise, low-contrast computed tomography (CT) of a ball, and CT of a liver lesion.

Citation Download Citation

Peter J. Yim "A resistive-network model for image segmentation", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691403 (11 March 2008); https://doi.org/10.1117/12.768108

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