Artif Intell Med - Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems.

Tópicos

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Resumo

JECTIVE: This paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research.MATERIALS AND METHODS: A novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems.RESULTS: The proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets.CONCLUSION: We developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images.

Resumo Limpo

jectiv paper present algorithm base multilevel watersh segment combin three fuzzi system segment larg number myelin nerv fiber microscop imag method can estim various geometr paramet myelin nerv fiber peripher nerv expect promis tool quantit assess myelin nerv fiber relat researchmateri method novel multilevel watersh scheme iter detect precandid nerv fiber immers level watersh segment extract initi axon locat obtain meaning myelin nerv fiber featur thereaft accord priori characterist myelin nerv fiber fuzzi rule reject unlik precandid collect set candid initi candid boundari refin fuzzi activ contour model flexibl deform contour accord observ featur nerv fiber final scan differ set fuzzi rule base priori properti myelin nerv fiber remov fals detect particl swarm optim method employ effici train larg number paramet propos fuzzi systemsresult propos method can automat segment transvers crosssect nerv fiber obtain optic microscop imag although microscop imag usual noisi weak variabl level contrast propos system can handl imag larg number myelin nerv fiber achiev high fiber detect ratio compar manual segment expert propos system achiev averag accuraci across differ data setsconclus develop imag segment system automat handl myelin nerv fiber microscop imag experiment result show efficaci system superior nerv fiber segment approach moreov propos method can extend applic automat segment microscop imag

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