NDLI: Automated detection of microaneurysms using Stockwell transform and statistical features

Content Provider	IET Digital Library
Author	Deepa, V. Kumar, C. Sathish Andrews, Sheena Susan
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.
Starting Page	1341
Ending Page	1348
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 8, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5672
Journal	IET Image Processing
Publisher Date	2019-04-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	1140 Retinal Colour Photographs Automated Detection Biology And Medical Computing Biomedical Measurement And Imaging Biomedical Optical Imaging Classifier Classifier Model Comparative Study Computer Vision And Image Processing Technique Diabetic Retinopathy Discrete Orthonormal Stockwell Diseased Retinal Image Diseases Earliest Pre-eminent Indicators Eye Feature Extraction Image Classification Image Texture MAs Medical Image Processing Microaneurysms Normal Image Optical And Laser Radiation Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Standard Fundus Image Statistical Feature Stockwell Transform Testing Textural Feature
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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