NDLI: BAT algorithm inspired retinal blood vessel segmentation

Content Provider	IET Digital Library
Author	Sathananthavathi, Vallikutti Indumathi, Ganesan
Abstract	The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.
Starting Page	2075
Ending Page	2083
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1266
Journal	IET Image Processing
Publisher Date	2018-07-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Retinal Blood Vessel Extraction BAT Algorithm Inspired Retinal Blood Vessel Segmentation Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Blood Vessels Classifier Error Minimisation Computer Vision And Image Processing Technique Digital Retinal Image Diseases Eye Feature Extraction Feature Selection Feature Set Feature Vector Image Classification Image Recognition Image Segmentation Knowledge Engineering Technique Learning in AI Local Feature Extraction Medical Analysis Medical Image Processing Morphological Feature Extraction Optical And Laser Radiation Optimisation Optimisation Technique Patient Diagnostic Method And Instrumentation Phase Feature Extraction Random Forest Classifier Retinal Diseases
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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