Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

2015 ◽  
pp. 379-388 ◽  
Author(s):  
Sarni Suhaila Rahim ◽  
Vasile Palade ◽  
Chrisina Jayne ◽  
Andreas Holzinger ◽  
James Shuttleworth
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Fuzzy Image

An Approach for Automatic Detection and Grading of Macular Edema

2017 ◽  
pp. 1677-1702
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images

2015 ◽  
Vol 44 ◽  
pp. 41-53 ◽  
Author(s):  
Roberto Rosas-Romero ◽  
Jorge Martínez-Carballido ◽  
Jonathan Hernández-Capistrán ◽  
Laura J. Uribe-Valencia
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images

scholarly journals Diabetic Retinopathy Diagnosing using Fuzzy Image Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4210-4215
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Expert System ◽  
Diagnostic System ◽  
Diabetic Patients ◽  
Input Output ◽  
Fuzzy Image ◽  
Retina Images ◽  
Oct Image

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.

Hypertensive Retinopathy Classification Using Improved Clustering Algorithm and the Improved Convolution Neural Network

2022 ◽  
pp. 119-131
Author(s):  
Bhimavarapu Usharani
Keyword(s):  
Image Processing ◽  
Clustering Algorithm ◽  
Activation Function ◽  
Current Approach ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Image Processing Techniques ◽  
Detection Systems ◽  
Processing Techniques ◽  
Fuzzy Image

Hypertensive retinopathy is a disorder that causes hypertension which includes abnormalities in the retina that triggers vision problems. An effective automatic diagnosis and grading of the hypertensive retinopathy would be very useful in the health system. This chapter presents an improved activation function on the CNN by recognizing the lesions present in the retina and afterward surveying the influenced retina as indicated by the hypertensive retinopathy various sorts. The current approach identifies the symptoms associated of retinopathy for hypertension. This chapter presents an up-to-date review on hypertensive retinopathy detection systems that implement a variety of image processing techniques, including fuzzy image processing, along various improved activation function techniques used for feature extraction and classification. The chapter also highlights the available public databases, containing eye fundus images, which can be currently used in the hypertensive retinopathy research.

Hybrid fuzzy image processing for situation assessment [diabetic retinopathy]

2000 ◽  
Vol 19 (1) ◽  
pp. 76-83 ◽  
Author(s):  
G. Zahlmann ◽  
B. Kochner ◽  
I. Ugi ◽  
D. Schuhmann ◽  
B. Liesenfeld ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Situation Assessment ◽  
Fuzzy Image

scholarly journals An Approach for Automatic Detection and Grading of Macular Edema

Ophthalmology ◽  
2018 ◽  
pp. 241-266
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images

2018 ◽  
Vol 39 ◽  
pp. 40-56 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Hiam Alquraan ◽  
Isam Abu-Qasmieh ◽  
Alaa Al-Badarneh
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Low Cost ◽  
Cost Effective ◽  
Support Vector ◽  
Fundus Images ◽  
Automated Identification ◽  
Diagnosis System ◽  
Cost Effective Method

Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 92.66%, and 91.73% and specificity of 89.83%, 91.26%, 91.96%, and 89.16% for ANN, and an accuracy of 90.0%,92.94%, 95.43%, and 97.92% and sensitivity of 89.34%, 93.26%, 95.72%, and 97.93% and specificity of 95.13%, 96.68%, 97.88%, and 99.05% for SVM, based on 5, 10, 15, and 31 number of selected features. The proposed system can detect glaucoma, diabetic retinopathy and normal cases with high accuracy and sensitivity using selected features, the performance of the system is high due to using of a huge fundus database.

scholarly journals Optic Disc Segmentation in Diabetic Retinopathy using Image Processing

2020 ◽  
pp. 236-240
Author(s):  
Prashant Vishwakarma ◽  
Somen Jaiswal ◽  
Jay Chandarana ◽  
Abhishek Vyas
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Optic Disk ◽  
Diagnostic Tools ◽  
Fundus Images ◽  
Macula Edema ◽  
Crucial Feature ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Diabetic Retinopathy and Glaucoma are optic diseases that involve optic disk identification, which is a crucial phase in the current diagnostic tools that can be computerized. When these diseases are identified early by any screening applications, measures may be taken to avoid blindness. Early indicators of the numerous illness such as Macula Edema, Diabetic Retinopathy and Glaucoma are the changes in the anatomy structures in the retina of the human eye which also has the inclusion of the retinal vasculature. Of these, the Optic Disc is the most crucial feature, as its visible factors are essential for the identification of glaucoma and other disease-related assessments called Diabetic Retinopathy. In this paper, we present methods to detect the likelihood of Diabetic Retinopathy being present from fundus images. This technique starts with pre-processing on the optic retinal image to concentrate on the main area of the disease that we need to identify. Afterwards we apply Image processing algorithms to detect the optic disk. Detecting the optic disc is vital because it is the origin of all the nerves and detecting the position and radius of optic disc can be used as the reference for approximating fovea i.e. a pit like area responsible for vision. Size and shape of optic disc is responsible for diagnosing the disease. Therefore, this paper addresses the analysis of different techniques to detect the optic disc.

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