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Real-Time Retinal Vessel Segmentation on High-Resolution Fundus Images Using Laplacian Pyramids

  • In ophthalmology, fundus images are commonly used to examine the human eye. The image data shows among others the capillary system of the retina. Recognising alternations in the retinal blood vessels is pivotal to diagnosing certain diseases. The visual inspection of those fundus images is a time-consuming process and a challenging task which has to be done by medical experts. Furthermore, rapid advances in medical imaging allow for generating fundus images of increased quality and resolution. Therefore, the support by computers for the analysis and evaluation of complex fundus image information is growing in importance and there is a corresponding need for fast and efficient algorithms. In this paper, we present a well-engineered, robust real-time segmentation algorithm which is adapted to the recent and upcoming challenges of high resolution fundus images. Thereby we make use of the multiscale representation of the Laplacian pyramid which is fast to compute and useful for detecting coarse as well as finely branched blood vessels.In ophthalmology, fundus images are commonly used to examine the human eye. The image data shows among others the capillary system of the retina. Recognising alternations in the retinal blood vessels is pivotal to diagnosing certain diseases. The visual inspection of those fundus images is a time-consuming process and a challenging task which has to be done by medical experts. Furthermore, rapid advances in medical imaging allow for generating fundus images of increased quality and resolution. Therefore, the support by computers for the analysis and evaluation of complex fundus image information is growing in importance and there is a corresponding need for fast and efficient algorithms. In this paper, we present a well-engineered, robust real-time segmentation algorithm which is adapted to the recent and upcoming challenges of high resolution fundus images. Thereby we make use of the multiscale representation of the Laplacian pyramid which is fast to compute and useful for detecting coarse as well as finely branched blood vessels. It is possible to process images of size 3504×2336 pixels in 0.8 s on a standard desktop computer and 0.3 on a Nvidia Titan XP GPU. By a detailed evaluation at hand of an accessible high-resolution data set we demonstrate that our approach is competitive in quality to state of the art methods for segmenting blood vessels but much faster.show moreshow less

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Metadaten
Author: Robert Dachsel, Annika Jöster, Michael BreußGND
DOI:https://doi.org/10.1007/978-3-030-34879-3_26
ISBN:978-3-030-34878-6
ISBN:978-3-030-34879-3
ISSN:0302-9743
ISSN:1611-3349
Title of the source (English):Image and Video Technology, 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings
Publisher:Springer
Place of publication:Cham
Editor: Chilwoo Lee, Zhixun Su, Akihiro Sugimoto
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2019
Tag:Laplacian pyramids Vessel segmentation High-resolution fundus images Real-time retinal imaging
First Page:337
Last Page:350
Series ; volume number:Lecture Notes in Computer Science book series ; volume 11854
Series ; volume number:Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series ; volume 11854
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Angewandte Mathematik
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