Morphological Contour Based Blood Vessel Segmentation in Retinal Images Using Otsu Thresholding

Retinal image analysis plays an important part in identifying various eye related diseases such as diabetic retinopathy (DR), glaucoma and many others. Accurate segmentation of blood vessels plays an important part in identifying the retinal diseases at an early stage. In this article, an unsupervised approach based on contour detection has been proposed for effective segmentation of retinal blood vessels. The proposed morphological contour-based blood vessel segmentation (MCBVS) method performs preprocessing using contrast limited adaptive histogram equalization followed by alternate sequential filtering to generate a noise-free image. The resultant image undergoes Otsu thresholding for candidate extraction followed by contour detection to properly segment the blood vessels. The MCBVS method has been tested on the DRIVE dataset and the experimental result shows that the proposed method achieved a sensitivity, specificity and accuracy of 58.79%, 90.77% and 86.7%, respectively. The MCBVS method performs better than the existing methods Sobel, Prewitt and Modified U-Net in terms of accuracy.