An algorithm combining two lesion-detection methods of retinal microaneurysms for the reduction of human workload

Ometto, G. and Bek, T. and Al-Diri, B. and Hunter, A. (2015) An algorithm combining two lesion-detection methods of retinal microaneurysms for the reduction of human workload. Acta Ophthalmologica, 93 (S255). ISSN 1755-3768

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Item Type:Article
Item Status:Live Archive

Abstract

Purpose

Reduction of workload in the detection of microaneurysms (MA) from retinal photographs is crucial for the diagnosis and screening of diabetic retinopathy. Automatic algorithms for the detection of retinal lesions can help reduce human intervention especially when the lesions are present in large numbers.
Methods

Two methods for lesion detection were combined in a single algorithm, one based on the analyses of the contrast between dark peak-points and surrounding circular regions, and a second one based on the correlation between the intensity values in the photographs and a MA-template. The two individual methods and the two methods combined were tested separately to compare their performance on retinal images from 26 high-risk patients.
Results

Both individual lesion-detection methods missed clustered MAs. With the exclusion of grouped lesions, the two methods combined showed higher sensitivity and precision than the contrast and template methods alone, identifying 22% and 13% more lesions respectively.
Conclusions

The combination of the two methods can provide repeatable detection of unclustered MAs in photographs from high-risk patients. Manual intervention is only required to select grouped MAs and to adjust the automatic results, considerably reducing human workload.

Additional Information:Special Issue: Abstracts from the 2015 European Association for Vision and Eye Research Conference, October 7-10, 2015, Nice, France
Keywords:Lesion detection, microaneurysms, retinal lesion segmentation, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:26902
Deposited On:07 Apr 2017 13:36

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