IEEE Trans Image Process - Segmentation of stochastic images with a stochastic random walker method.

Tópicos

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Resumo

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 algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.

Resumo Limpo

present extens random walker segment imag uncertain gray valu grayvalu uncertainti may result nois imag artifact general measur error imag acquisit process purpos quantifi influenc grayvalu uncertainti onto result use random walker segment random walker segment weight graph built imag edg weight depend imag gradient pixel given seed region probabl evalu random walk graph start pixel end one seed region extend method imag uncertain gray valu end consid pixel valu random variabl rvs thus introduc notion stochast imag end stochast weight graph random walker segment stochast partial differenti equat pde solv discret rvs stochast pde method general polynomi chao combin recent develop numer method discret stochast pdes interact segment algorithm result algorithm allow detect region segment result high influenc uncertain pixel valu thus give reliabl estim result segment furthermor allow determin probabl densiti function segment object volum

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