Automated detection of microaneurysms using Stockwell transform and statistical features. Issue 8 (22nd May 2019)

• Record Type: Journal Article
• Authors: Deepa, V.; Sathish Kumar, C.; Susan Andrews, Sheena
• Is Part Of: IET image processing. Volume 13:Issue 8(2019)
• Page Start: 1341
• Page End: 1348
• Publication Date: 2019-05-22
• Subjects: biomedical optical imaging -- eye -- image texture -- feature extraction -- diseases -- image classification -- medical image processing; automated detection -- microaneurysms -- Stockwell transform -- statistical features -- MAs -- earliest pre‐eminent indicators -- diabetic retinopathy -- DR -- standard fundus images -- discrete orthonormal Stockwell -- normal images -- diseased retinal images -- feature extraction -- classifier models -- testing -- 1140 retinal colour photographs -- comparative study -- classifiers -- textural features; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5672
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Microaneurysms (MAs) are the earliest pre‐eminent indicators of diabetic retinopathy (DR) and are hard to distinguish for ophthalmologists on standard fundus images. This study proposes a method based on discrete orthonormal Stockwell transform and statistical features for discriminating between normal and diseased retinal images. Feature extraction by the two different approaches is consolidated and a total of 24 features are extracted for classifier models. Training and testing of the proposed method have been accomplished using 1140 retinal colour photographs. A comparative study using eight best‐known classifiers is showcased for detection of MAs and the performance of the classifiers is evaluated using retinal images by performing ten‐fold cross‐validation procedure. Simulation results demonstrate the efficiency and adequacy of the proposed method which mainly characterise the textural features. The proposed method is compared with existing algorithms and the results show that the algorithm detects DR with high veracity. With the high accuracy and positive prediction, the proposed system assures promising results in early diagnosis of DR.