Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images

In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verificat...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2003-01, Vol.25 (1), p.131-137
Main Authors: Xiaoyi Jiang, Mojon, D.
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Application software
Biomedical imaging
Cameras
Histograms
Image segmentation
Intelligence
Lighting
Pattern analysis
Performance evaluation
Pixel
Reflectivity
Retina
Retinal images
Shape
Thresholds
Vessels
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Summary:In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2003.1159954