Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement

Objective: Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed. Methods: The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure. Results: Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively. Conclusion: The segmentation accuracy is high, and better blood vessel segmentation results can be obtained. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 How to cite this:Hao Y, Xie H, Qiu R. Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement. Pak J Med Sci. 2021;37(6):1595-1599. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

New Hypertensive Retinopathy Grading Based on the Ratio of Artery Venous Diameter from Retinal Image

Medical research indicated that narrowing of the retinal blood vessels might be an early indicator of cardiovascular diseases; one of them is hypertensive retinopathy. This paper proposed the new staging method of hypertensive retinopathy by measure the ratio of diameter artery and vein (AVR). The dataset used in this research is the public Messidor color fundus image dataset. The proposed method consists of image resizing using bicubic interpolation, optic disk detection, a region of interest computation, vessel diameter measuring, AVR calculation, and grading the new categories of Hypertensive Retinopathy based on Keith-Wagener-Barker categories. The experiments show that the proposed method can determine the stage of hypertensive retinopathy into new categories.

A Generic Study on Diabetic Retinopathy Detection

Diabetic Retinopathy is one of a major cause of visual defects in the growing population which affects the light perception part of the retina. It affects both types of diabetes mellitus. It occurs when high blood sugar levels damage the blood vessels in retina causing them to swell and leak or stop blood flow through them. It starts with no or mild vision problems and can eventually cause blindness if not treated.With the advancements in technology, automated detection and analysis of the stage of Diabetic Retinopathy will help in early detection and treatment. Almost 75% of the patients with diabetes have the risk of being affected by this disease. With early detection this disease can be prevented. Currently DR detection is a traditional and manual, time-consuming process. It requires a trained technician to analyze the color fundus image of retina.With the ever growing population, DR detection is very high in demand to prevent blindness. In this paper we aim to review the existing methodologies and techniques for detection. Also a system for the detection of the 4 stages of DR is proposed.

Segmentation of Optic Disc From Fundus Image Based on Morphology and SVM Classifier

Color fundus image is the most basic way to diagnose diabetic retinopathy, papillary edema, and glaucoma. In particular, since observing the morphological changes of the optic disc is conducive to the diagnosis of related diseases, accurate and effective positioning and segmentation of the optic disc is an important process. Optic disc segmentation algorithms are mainly based on template matching, deformable model and learning. According to the character that the shape of the optic disc is approximately circular, this proposed research work uses Kirsch operator to get the edge of the green channel fundus image through morphological operation, and then detects the optic disc by HOUGH circle transformation. In addition, supervised learning in machine learning is also applied in this chapter. First, the vascular mask is obtained by morphological operation for vascular erasure, and then the SVM classifier is segmented by HU moment invariant feature and gray level feature. The test results on the DRIONS fundus image database with expert-labeled optic disc contour show that the two methods have good results and high accuracy in optic disc segmentation. Even though seven different assessment parameters (sensitivity [Se], specificity [Sp], accuracy [Acc], positive predicted value [Ppv], and negative predicted value [Npv]) are used for performance assessment of the algorithm. Accuracy is considered as the criterion of judgment in this chapter. The average accuracy achieved for the nine random test set is 97.7%, which is better than any other classifiers used for segmenting Optical Disc from Fundus Images.