Comput Math Methods Med - Bayesian method with spatial constraint for retinal vessel segmentation.

Tópicos

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Resumo

A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.

Resumo Limpo

bayesian method spatial constraint propos vessel segment retin imag propos model make assumpt posterior probabl pixel depend posterior probabl neighbor pixel energi function defin propos model appli modifi level set approach minim propos energi function can identifi blood vessel retin imag evalu develop method done real retin imag drive databas stare databas perform analyz compar publish method use number measur includ accuraci sensit specif propos approach prove effect two databas averag accuraci sensit specif drive databas respect stare databas respect perform better vessel segment method

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