Comput Math Methods Med - Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation.

Tópicos

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Resumo

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

Resumo Limpo

automat analysi retin blood vessel play import role computeraid diagnosi paper introduc probabilist trackingbas method automat vessel segment retin imag take account vessel edg detect whole retin imag handl differ vessel structur track process bayesian method maximum posteriori map criterion use detect vessel edg point experiment evalu track algorithm perform real retin imag three public avail databas stare hoover et al drive staal et al review aldiri et al got high accuraci vessel segment width measur vessel structur identif sensit specif stare respect drive sensit specif

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