Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Sub-Image Classification
Top Cited Papers
- 8 July 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 19 (3), 1
- https://doi.org/10.1109/jbhi.2014.2335617
Abstract
This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.This publication has 27 references indexed in Scilit:
- Blood vessel segmentation methodologies in retinal images – A surveyComputer Methods and Programs in Biomedicine, 2012
- An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel SegmentationIEEE Transactions on Biomedical Engineering, 2012
- Detection of New Vessels on the Optic Disc Using Retinal PhotographsIEEE Transactions on Medical Imaging, 2010
- Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by ReconstructionIEEE Transactions on Biomedical Engineering, 2010
- A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based FeaturesIEEE Transactions on Medical Imaging, 2010
- Detection of Blood Vessels in the Retina Using Gabor FiltersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstructionIEEE Transactions on Medical Imaging, 2006
- Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancyIEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
- A model based method for retinal blood vessel detectionComputers in Biology and Medicine, 2004
- Locating blood vessels in retinal images by piecewise threshold probing of a matched filter responseIEEE Transactions on Medical Imaging, 2000