Segmentation of retina images to detect abnormalities arising from diabetic retinopathy

Aim: To design diagnostic expert system using fuzzy image processing for diabetic retinopathy, measures diabetic eye morbidity. Method: From this research paper, diagnosing diabetic retinopathy using fuzzy image processing for diabetic patients. Firstly collection of OCT images of the patient who has diabetic retinopathy. Author’s proposed method finds out the edge detection of the OCT image. Then fuzzy logic is applied on that result of image processing. Design a fuzzy rules and input- output parameter. This method gives accurate diagnosing the diabetic retinopathy from the image of the patient’s retina images. Result: This diagnostic system gives patient’s eye morbidity, vision threatening of the diabetic patients. In the result, edges of the retina images, and from that retinal ruptures, thickness of the proliferative in the retina. From these result, diagnostic of diabetic retinopathy conditions such as PDR, NPDR, and NORMAL, and CSME in the diabetic patients. Conclusion: author has design diagnostic system for endocrinologist and ophthalmology to diagnosed diabetic retinopathy in the patients. From this system doctors don’t need patients for diagnosing purposed.

Download Full-text

VEA: Vessel Extraction Algorithm by Active Contour Model and a Novel Wavelet Analyzer for Diabetic Retinopathy Detection

International Journal of Image and Graphics ◽

10.1142/s0219467818500080 ◽

2018 ◽

Vol 18 (02) ◽

pp. 1850008 ◽

Cited By ~ 7

Author(s):

Swarup Kr Ghosh ◽

Anupam Ghosh ◽

Amlan Chakrabarti

Keyword(s):

Diabetic Retinopathy ◽

Active Contour Model ◽

Back Propagation ◽

Diagnostic System ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Circulation System ◽

Retinal Microvasculature ◽

Retina Images

The process of retinal vessel segmentation is important for detection of various eye conditions including the effect of diabetes on eyes, or diabetic retinopathy. As we know, the retinal microvasculature is unique since it is the only part of the human circulation system that can be directly and non-evasively visualized in vivo; readily photographed as well as subjected to digital image analysis. This paper explores a new technique for detecting the idiosyncrasies of retina images, for which we have reviewed some well-known image segmentation algorithms that help in detecting retinal abnormalities. In this work, we have also focused on the extraction of the vessel from retina images and developed an automated diagnostic system for diabetic retinopathy. This paper represents techniques, such as the snake model that was used for auto-extraction of retinal blood vessels and use of wavelet decomposition and back propagation neural network to extract the retinal vessels features and analyze the dataset. Finally, an analysis of performance of the vessel segmentation algorithm and wavelet analysis on standard image databases has been done. In this context, we have used F-score for validation of the result.

Download Full-text

A recursive support vector machine (RSVM) algorithm to detect and classify diabetic retinopathy in fundus retina images

Biomedical Research ◽

10.4066/biomedicalresearch.29-16-2328 ◽

2018 ◽

Cited By ~ 1

Author(s):

K Malathi ◽

R Nedunchelian

Keyword(s):

Support Vector Machine ◽

Diabetic Retinopathy ◽

Support Vector ◽

Retina Images

Download Full-text

Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier

PeerJ Computer Science ◽

10.7717/peerj-cs.456 ◽

2021 ◽

Vol 7 ◽

pp. e456

Author(s):

Lakshmana Kumar Ramasamy ◽

Shynu Gopalan Padinjappurathu ◽

Seifedine Kadry ◽

Robertas Damaševičius

Keyword(s):

Diabetic Retinopathy ◽

Diabetes Complications ◽

Detection Method ◽

Retinal Images ◽

Gray Level ◽

Sequential Minimal Optimization ◽

Ridgelet Transform ◽

The World ◽

Disease Stages ◽

Retina Images

Diabetes is one of the most prevalent diseases in the world, which is a metabolic disorder characterized by high blood sugar. Diabetes complications are leading to Diabetic Retinopathy (DR). The early stages of DR may have either no sign or cause minor vision problems, but later stages of the disease can lead to blindness. DR diagnosis is an exceedingly difficult task because of changes in the retina during the disease stages. An automatic DR early detection method can save a patient's vision and can also support the ophthalmologists in DR screening. This paper develops a model for the diagnostics of DR. Initially, we extract and fuse the ophthalmoscopic features from the retina images based on textural gray-level features like co-occurrence, run-length matrix, as well as the coefficients of the Ridgelet Transform. Based on the retina features, the Sequential Minimal Optimization (SMO) classification is used to classify diabetic retinopathy. For performance analysis, the openly accessible retinal image datasets are used, and the findings of the experiments demonstrate the quality and efficacy of the proposed method (we achieved 98.87% sensitivity, 95.24% specificity, 97.05% accuracy on DIARETDB1 dataset, and 90.9% sensitivity, 91.0% specificity, 91.0% accuracy on KAGGLE dataset).

Download Full-text

EXUDATES DETECTION AND CLASSIFICATION ALGORITHM OF DIABETIC PATIENTS' RETINA IMAGES

Journal of Circuits System and Computers ◽

10.1142/s0218126612500879 ◽

2013 ◽

Vol 22 (02) ◽

pp. 1250087

Author(s):

VESNA ZELJKOVIĆ ◽

MILENA BOJIC ◽

CLAUDE TAMEZE ◽

VENTZESLAV VALEV

Keyword(s):

Diabetic Retinopathy ◽

Diabetic Patients ◽

Diabetic Retinopathy Screening ◽

Automated Algorithm ◽

Chronic Hyperglycemia ◽

Clinical Environments ◽

Retinal Fundus ◽

Retina Images

The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart and blood vessels. The regular examination of diabetic patients can potentially reduce the risk of vision impairment and in the last instance blindness. Early diabetic retinopathy detection enables application of laser therapy treatment in order to prevent or delay loss of vision. The diagnostics and detection of diabetic retinopathy is performed by specialized ophthalmologists manually and represents expensive procedure. Automatic exudates detection and retina images classification would be helpful for reducing diabetic retinopathy screening costs and encouraging regular examinations. We proposed the automated algorithm that applies mathematical modeling which enables light intensity levels emphasis, easier exudates detection, efficient and correct classification of retina images. The proposed algorithm is robust to various appearance changes of retinal fundus images which are usually processed in clinical environments.

Download Full-text