Open AccessAutomated detection of microaneurysms using Stockwell transform and statistical features
Author(s) -
Deepa V.,
Sathish Kumar C.,
Susan Andrews Sheena
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5672
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , feature extraction , classifier (uml) , fundus (uterus) , computer vision , medicine , ophthalmology
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.