Supervised Retinal Vessel Segmentation Based Average Filter and Iterative Self Organizing Data Analysis Technique

Retinal fundus is the inner surface of the eye associated with the lens. The identification of disease needs some parts of retinal fundus, such as blood vessel. Blood vessels are part of circulation system which functions to supply blood to retina area. This research proposed a method for segmentation of blood vessel in retinal image with Average Filter and Iterative Self-Organizing Data Analysis (ISODATA) Technique. The first step with the input image changed to Gamma Correction, increasing contrast with Contrast Limited Adaptive Histogram Equalization (CLAHE), the filtering process with Average Filter. The segmentation is used for ISODATA. Region of Interest was applied to take the center of a vessel object and remove the background. In the final stage, the process of noise reduction and removal of small pixel values with Median Filter and Closing Morphology. Datasets used in this research were DRIVE and STARE. The average result was obtained for STARE dataset with an accuracy of 94.41%, Sensitivity of 55.57%, Specification of 98.31%, F1 Score of 64.81% while for the DRIVE dataset with accuracy of 94.78%, Sensitivity of 43.46%, Specification of 99.81%, and F1 Score of 59.39%.

Automated summary

This study proposed a new method for segmenting blood vessels in retinal images using various image processing techniques and data analysis methods. The experimental results showed high accuracy and specificity on two datasets.