研究目的
Investigating the automated extraction of retinal blood vessels for medical analysis of retinal diseases using BAT algorithm and random forest classifier.
研究成果
The proposed retinal vessel segmentation method based on BAT algorithm and random forest classifier achieves high sensitivity and accuracy, outperforming existing methods. It is robust against various imaging issues and shows promise for future extensions like artery vein classification.
研究不足
The method may fail in regions with red lesions such as microaneurysms and haemorrhages due to the same intensity level of vessels.
1:Experimental Design and Method Selection:
The methodology involves the use of BAT algorithm for feature selection and random forest classifier for segmentation.
2:Sample Selection and Data Sources:
Publicly available databases DRIVE and STARE are used for validation.
3:List of Experimental Equipment and Materials:
Canon CR5 non-mydriatic three charge-coupled device camera, TopCon TRV-50 fundus camera.
4:Experimental Procedures and Operational Workflow:
Feature extraction from contrast enhancement, background subtraction, phase congruency, morphological transformations, vessel profile, Hessian and gradient details.
5:Data Analysis Methods:
Performance analysis based on sensitivity, accuracy, and specificity measures.
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