Open AccessA Wide-Ranging Review on Diabetic Retinotherapy
Author(s) -
Toufique Ahmed Soomro,
Ahsin Murtaza Bughio,
Shahid Hussain Siyal,
Ali Anwar Panwar,
Nasreen Nizamani
Publication year - 2020
Publication title -
quaid-e-awam university research journal of engineering science and technology
Language(s) - English
Resource type - Journals
eISSN - 2523-0379
pISSN - 1605-8607
DOI - 10.52584/qrj.1802.25
Subject(s) - diabetic retinopathy , fundus (uterus) , computer science , artificial intelligence , computer vision , segmentation , image processing , optometry , image segmentation , medicine , ophthalmology , image (mathematics) , diabetes mellitus , endocrinology
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.