Retinal Vessel Segmentation to Support Foveal Avascular Zone Detection

Purpose: This study aims to perform retinal vessel segmentation to support foveal avascular zone detection. Methodology: The proposed approach consists of a multi-stage image processing approach, including preprocessing, image quality enhancementt, and segmentation of retinal blood vessel using matched filter and length filter techniques.Findings: The proposed framework has achieved remarkable results with an average sensitivity, specificity, and accuracy of 77.99%, 86.43%, and 85.24%, respectively.Value: This achievement has the potential to significantly enhance the accuracy and efficiency of detecting and diagnosing medical conditions related to the retina, improving the quality of life for countless individuals

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods

A modified dolph-chebyshev type ii function matched filter for retinal vessels segmentation

In this paper, we present a new unsupervised algorithm for retinal vessels segmentation. The algorithm utilizes a directionally sensitive matched filter bank using a modified Dolph-Chebyshev type II basis function and a new method to combine the matched filter bank’s responses. Fundus images from the DRIVE and STARE databases, as well as high-resolution fundus images from the HRF database, are utilized to validate the proposed algorithm. The results that we achieve on the three databases (DRIVE: Sensitivity = 0.748, F1-score = 0.786, G-score = 0.856, Matthews Correlation Coefficient = 0.758; STARE: Sensitivity = 0.793, F1-score = 0.780, G-score = 0.877, Matthews Correlation Coefficient = 0.756; HRF: Sensitivity = 0.804, F1-score = 0.764, G-score = 0.883, Matthews Correlation Coefficient = 0.741) are higher than many other competing methods.Published versio

A Modified Dolph-Chebyshev Type II Function Matched Filter for Retinal Vessels Segmentation

In this paper, we present a new unsupervised algorithm for retinal vessels segmentation. The algorithm utilizes a directionally sensitive matched filter bank using a modified Dolph-Chebyshev type II basis function and a new method to combine the matched filter bank’s responses. Fundus images from the DRIVE and STARE databases, as well as high-resolution fundus images from the HRF database, are utilized to validate the proposed algorithm. The results that we achieve on the three databases (DRIVE: Sensitivity = 0.748, F1-score = 0.786, G-score = 0.856, Matthews Correlation Coefficient = 0.758; STARE: Sensitivity = 0.793, F1-score = 0.780, G-score = 0.877, Matthews Correlation Coefficient = 0.756; HRF: Sensitivity = 0.804, F1-score = 0.764, G-score = 0.883, Matthews Correlation Coefficient = 0.741) are higher than many other competing methods