Comput Math Methods Med - Retinal image graph-cut segmentation algorithm using multiscale Hessian-enhancement-based nonlocal mean filter.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a new method to enhance and extract the retinal vessels. First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel. By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time. After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel structures. Finally, an accurate graph-cut segmentation step is performed using the result of previous symmetry transformation as an initial. We test the proposed approach on the publicly available databases: DRIVE. The experimental results show that our method is quite effective.

Resumo Limpo

propos new method enhanc extract retin vessel first employ multiscal hessianbas filter comput maximum respons vessel like function pixel step blood vessel differ width signific enhanc adopt nonloc mean filter suppress nois enhanc imag maintain vessel inform time radial gradient symmetri transform adopt suppress nonvessel structur final accur graphcut segment step perform use result previous symmetri transform initi test propos approach public avail databas drive experiment result show method quit effect

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