Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening.
Jayne, Chrisina; Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James
Sarni Suhaila Rahim
Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.
|Journal Article Type||Article|
|Publication Date||Jul 31, 2016|
|Journal||Neural computing and applications|
|Publisher||Springer (part of Springer Nature)|
|Peer Reviewed||Peer Reviewed|
|Institution Citation||RAHIM, S.S., JAYNE, C., PALADE, V. and SHUTTLEWORTH, J. 2016. Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. Neural computing and applications [online], 27(5), pages 1149-1164. Available from: https://doi.org/10.1007/s00521-015-1929-5.|
|Keywords||Diabetic retinopathy; Eye screening; Colour fundus images; Image processing; Micro aneurysms|
RAHIM 2015 Automatic detection of microaneurysms
You might also like
Multiple fake classes GAN for data augmentation in face image dataset.
Overlap-based undersampling for improving imbalanced data classification.
Toward video tampering exposure: inferring compression parameters from pixels.
Deep imitation learning with memory for robocup soccer simulation.
Symbols classification in engineering drawings.