NDLI: An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

Content Provider	IET Digital Library
Author	Zhu, Chengzhang Zou, Beiji Xiang, Yao Cui, Jinkai Wu, Hui
Abstract	An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree (CART). Finally, an AdaBoost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction (DRIVE) database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new Retinal images for screening (RIS) database. The average accuracy, sensitivity, and specificity of improved method are 0.9535, 0.8319 and 0.9607, respectively. It has potential applications for computer-aided diagnosis and disease screening.
Starting Page	503
Ending Page	511
Page Count	9
ISSN	10224653
Volume Number	25
e-ISSN	20755597
Issue Number	Issue 3, May (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/25/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2016.05.016
Journal	Chinese Journal of Electronics
Publisher Date	2016-05-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	AdaBoost Classifier Biology And Medical Computing Biomedical Measurement And Imaging Blood Vessels CART Classification And Regression Tree Color Fundus Image Computer Vision And Image Processing Technique Digital Retinal Image For Vessel Extraction Database DRIVE Database Ensemble Retinal Vessel Segmentation Eye Feature Extraction Feature Vector Extraction Health Physics Image Classification Image Colour Analysis Image Recognition Image Segmentation Iterative Training Knowledge Engineering Technique Learning in AI Lesions Local Feature Medical And Biomedical Uses of Field Medical Image Processing Morphological Transformation Patient Diagnostic Method And Instrumentation Radiations Radioactivity Retinal Image For Screening Retinal Image Pixel RIS Database Supervised Learning
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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