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Glaucoma, hypertension, obesity, diabetes, etc. are the widely spread severe diseases nowadays. Glaucoma, hypertension, and diabetes are the main cause of vision loss, whereas diabetes, obesity, and hypertension are the basis of several other highly dangerous diseases. Blood vessels of a retina contain information about these critical diseases. Health professionals use this information to detect and diagnose these diseases. Hence, it is essential to segment retinal blood vessels. Many researchers have proposed different matched filter methods to segment retinal blood vessels, but their matched filter kernels are not appropriate to vessel intensity profile due to which performances of these methods are relatively low. Also, the quality of retinal images directly affects the accuracy of segmentation. Therefore, retinal images must be of good quality. Existing methods for retinal image enhancement are less efficient. The proposed method introduces the Multiscale switching morphological operator (MSMO) with Wald probability distribution function-based new matched filter for better enhancement and segmentation of retinal vessels in this paper. The proposed method is tested on two known datasets: DRIVE and STARE. The average value of the precision, specificity, accuracy, MSE and MAD of the proposed method are 83.74%, 98.80%, 95.58%, 0.0442, 0.0366 for DRIVE and 71.24%, 97.67%, 94.89%, 0.0511, 0.015 for STARE dataset. Results demonstrate significant improvement in the performance of the proposed method over existing state-of-the-art methods. The causes of improved performance are enhanced retinal images due to using a better pre-processing method and close matching of Wald matched filter kernel with vessel intensity profile.

Retinal blood vessels segmentation using Wald PDF and MSMO operator