scholarly journals Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

2017 ◽  
Vol 166 (8) ◽  
pp. 38-43 ◽  
Author(s):  
R. Geetha ◽  
Sugirtharani S. ◽  
Lakshmi B.
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Automatic Detection ◽  
Fundus Images ◽  
Data Mining Techniques ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals A Wide-Ranging Review on Diabetic Retinotherapy

2020 ◽  
Vol 18 (02) ◽  
pp. 169-187
Author(s):  
Toufique Ahmed Soomro ◽  
Ahsin Murtaza Bughio ◽  
Shahid Hussain Siyal ◽  
Ali Anwar Panwar ◽  
Nasreen Nizamani
Keyword(s):  
Image Processing ◽  
Blood Vessels ◽  
Detection System ◽  
Eye Diseases ◽  
Fundus Images ◽  
The Past ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Computerized System ◽  
Automatic Algorithm

Diabetic Retinopathy (DR) is one of the major eye diseases that causes damage to retina of the human eye ball due to the rupture of tiny blood vessels. DR is identified by the ophthalmologists on the basis of various specifications i.e., textures, blood vessels and pathologies. The ophthalmologists are recently considering software for eye diseases detection based on image processing designed by the computing techniques and bio-medical images. In the analysis of medical imaging, traditional techniques of image processing and computer vision have played an important role in the field of ophthalmology. From the past two decades, there is a tremendous advancement in the development of computerized system for DR detection. This paper comprises the five parts of analysis on image based retinal detection DR, named as review of low varying contrast techniques of the retinal fundus Images (RFI), review of noise effect in the fundus images, review of pathology detection method from the retinal fundus images, review of blood vessels extraction from the RFI, and review of automatic algorithm for the DR detection. This paper presents a comprehensive detail to each problem in the retinal images. The procedures that are currently utilized to analyze the contrast issue and noise issues are discussed in detail. The paper also explains the techniques used for segmentation. In the end, the recent automated detection system of related eye diseases or DR is described.

scholarly journals Automatic detection and classification of diabetic retinopathy from retinal fundus images

2021 ◽  
Author(s):  
Abdullah Biran
Keyword(s):  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Histogram Equalization ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

scholarly journals An Automatic Image Processing System for Glaucoma Screening

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ahmed Almazroa ◽  
Sami Alodhayb ◽  
Kaamran Raahemifar ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Processing System ◽  
Fundus Images ◽  
Type Ii ◽  
Fuzzy Approach ◽  
Glaucoma Screening ◽  
Automatic Image Processing ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm’s accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm’s accuracy. The algorithm’s best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.

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