RETICA is a very good algorithm for Micro-anurisum detection in DR images.

I am giving you the link to our paper published in 2011. This paper also lists other useful references.

AGR

http://mile.ee.iisc.ernet.in/mile/publications/MedicalImageProcessing.html

Hi,

Here is a very specific and simple filter to implement: http://scitation.aip.org/content/aapm/journal/medphys/30/8/10.1118/1.1581411.

I hope it helps.

A similar question arose a couple of months ago. As an

exercise I wrote a C# program that uses an astronomical

approach, using a star finder described in

http://www.aerith.net/misao/report/seiichi/master/970917-english/node3.html

http://www.augos.com/temp/VeneFinder.zip

Regards,

Joachim

Br J Ophthalmol doi:10.1136/bjophthalmol-2015-307341 Clinical science

Individualised risk assessment for diabetic retinopathy and optimisation of screening intervals: a scientific approach to reducing healthcare costs

S H Lund1,

T Aspelund1,2,

P Kirby3,

G Russell3,

S Einarsson2,

O Palsson2,

E Stefánsson1,2

+ Author Affiliations

1Faculty of Medicine, University of Iceland, Reykjavik, Iceland

2Risk ehf, Reykjavik, Iceland

3Health Intelligence plc, Cambridge, UK

Correspondence to Professor Einar Stefánsson, Department of Ophthalmology, University of Iceland, National University Hospital, 101 Reykjavík 101, Iceland; [email protected]

Received 26 June 2015

Revised 10 August 2015

Accepted 19 August 2015

Published Online First 16 September 2015

Abstract

Objective To validate a mathematical algorithm that calculates risk of diabetic retinopathy progression in a diabetic population with UK staging (R0–3; M1) of diabetic retinopathy. To establish the utility of the algorithm to reduce screening frequency in this cohort, while maintaining safety standards.

Research design and methods The cohort of 9690 diabetic individuals in England, followed for 2 years. The algorithms calculated individual risk for development of preproliferative retinopathy (R2), active proliferative retinopathy (R3A) and diabetic maculopathy (M1) based on clinical data. Screening intervals were determined such that the increase in risk of developing certain stages of retinopathy between screenings was the same for all patients and identical to mean risk in fixed annual screening. Receiver operating characteristic curves were drawn and area under the curve calculated to estimate the prediction capability.

Results The algorithm predicts the occurrence of the given diabetic retinopathy stages with area under the curve =80% for patients with type II diabetes (CI 0.78 to 0.81). Of the cohort 64% is at less than 5% risk of progression to R2, R3A or M1 within 2 years. By applying a 2 year ceiling to the screening interval, patients with type II diabetes are screened on average every 20 months, which is a 40% reduction in frequency compared with annual screening.

Conclusions The algorithm reliably identifies patients at high risk of developing advanced stages of diabetic retinopathy, including preproliferative R2, active proliferative R3A and maculopathy M1. Majority of patients have less than 5% risk of progression between stages within a year and a small high-risk group is identified. Screening visit frequency and presumably costs in a diabetic retinopathy screening system can be reduced by 40% by using a 2 year ceiling. Individualised risk assessment with 2 year ceiling on screening intervals may be a pragmatic next step in diabetic retinopathy screening in UK, in that safety is maximised and cost reduced by about 40%.

Epidemiology

Retina

Clinical Trial

Diagnostic tests/Investigation

Public health

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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