Ocular Channel Picture Integrity's Effect on Ocular Vessel Partitioning

Author(s): Dr. Tausif Khan*

Ocular vessel segmentation is important for the detection of Ocular vessels in various eye diseases, and a consistent computational method is required for automated eye disease screening. Although many methods have been implemented to segment Ocular vessels, these methods have only gained accuracy and lack of good sensitivity due to the consistency of segmentation of Ocular vessels. Another major cause of low sensitivity is proper techniques for handling the problem of low contrast variations. In this study, we proposed his five-step method to assess the effect of Ocular vascular coherence on Ocular vascular segmentation. The technique proposed for Ocular vessels includes four steps, known as preprocessing modules. These four stages of the preprocessing module deal with Ocular image processing in the first stage, morphological operations used in the second stage to deal with non-uniform illumination and noise problems, and principal component analysis in the third stage. Convert the image to grayscale using (PCA). The fourth step is to use anisotropic diffusion filtering and test its various schemes to obtain better coherent images with the optimized anisotropic diffusion filtering, thereby improving the coherence of the Ocular vessels. It is the main step that contributes to the final step included double thresholding with a morphological image reconstruction technique to generate segmented images of vessels. The performance of the proposed method is verified on publicly accessible databases called DRIVE and STARE. The sensitivity values of 0.811 and 0.821 for STARE and DRIVE are comparable or superior to other existing methods and the comparable accuracy values for STARE and DRIVE databases using existing methods are 0.961 and 0.954. This proposed new method of segmenting Ocular vessels can help medical professionals diagnose ocular diseases and recommend timely treatments.