Segmentation of stochastic images with a stochastic random walker method

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Torben Pätz, Tobias Preusser

Abstract

We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmentation algorithm. The resulting a...Continue Reading

References

Oct 27, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Leo Grady

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Citations

Feb 26, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jianbing ShenXuelong Li
Aug 21, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Fanman MengQingbo Wu

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