Abstract
We propose a novel approach to blood vessel detection in retinal images using shape-based multi-threshold probing. On an image set with hand-labeled ground truth our algorithm quantitatively demonstrates superior performance over the basic thresholding and another method recently reported in the literature. The core of our algorithm, classification-based multi-threshold probing, represents a general framework of segmentation that has not been explored in the literature thus far. We expect that the framework may be applied to a variety of other tasks.
The work was supported by the Stiftung OPOS Zugunsten von Wahrnehmungsbehinderten, St. Gallen, Switzerland.
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Jiang, X., Mojon, D. (2001). Blood Vessel Detection in Retinal Images by Shape-Based Multi-Threshold Probing. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_6
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DOI: https://doi.org/10.1007/3-540-45404-7_6
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