Abstract
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.
Highlights
Retinal vessel segmentation plays an important role in medical image processing
In order to detect the boundaries of the blood vessels, the modified level set approach is used for solving the energy function minimization problem
To overcome the drawback that the spatial information is not taken into account, the proposed model exploits the spatial information
Summary
Retinal vessel segmentation plays an important role in medical image processing. It can provide much help for the detection of eye diseases and other medical diagnosis. The algorithm based on pattern recognition can detect or classify the retinal blood vessel features and the background. This group of algorithms can be divided into two categories: supervised and unsupervised approaches. The matched filtering approaches [9,10,11] are popular methods to detect and measure blood vessels. The model-based approaches are very popular techniques for image segmentation and have been used for retinal vessel segmentation. Graph-based approach is very popular and interesting method for image segmentation and has been applied to vessel boundary detection [34]. In order to detect the boundaries of the blood vessels, the modified level set approach is used for solving the energy function minimization problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
