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Paper Title: Development Of Ocular Fundus Assessment For Early Detection Of Diabetic Retinopathy
Authors Name: Mr. Rajasekaran T , Ms. Sharmeli , Ms. Shunmathi Sivam B , Ms. Selva Shobika T
Unique Id: IJRTI1803015
Published In: Volume 3 Issue 3, March-2018
Abstract: Automated retinal image analysis is becoming an imperative screening tool for early revealing of certain risks and diseases like Diabetic Retinopathy. Diabetic Retinopathy (DR) is the prominent cause of blindness in the world. Early detection of diabetic retinopathy can provide operative treatment. Early treatment can be conducted from detection of microaneurysms. Microaneurysms are the earliest clinical sign of diabetic retinopathy and they appear as small red spots on retinal fundus images.Microaneurysms are reddish in colour with a diameter less than 125 μm. The existing trained eye care specialists are not able to screen the growing number of diabetic patients. So there is a need to develop a technique that is capable to detect microaneurysms as a part of diagnosis system, so that medical professionals are able to diagnose the stage of the disease with ease. Automated microaneurysm detection can decrease the workload of ophthalmologists and cost in DR screening system. Early automated microaneurysms detection can help in reducing the incidence of blindness. In this project, we review and analyse the techniques, algorithms and methodologies used for the detection of microaneurysms from diabetic retinopathy retinal fundus images.
Keywords: DR(DiabeticRetinopathy),fundus image, Microaneurysms, Blood vessel segmentation, Conditional Random Fields, Structured Output SVM.
Cite Article: "Development Of Ocular Fundus Assessment For Early Detection Of Diabetic Retinopathy", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.3, Issue 3, page no.92 - 97, March-2018, Available :
Downloads: 000803
Publication Details: Published Paper ID: IJRTI1803015
Registration ID:180055
Published In: Volume 3 Issue 3, March-2018
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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