A review of vessel extraction techniques and algorithms
By Cemil Kirbas, Francis K. H. Quek. CSUR, 2004
1,084 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/48404b15b16038ab30349a6b10d9d1773353b2a4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Joachim Hornegger
- Georg Michelson
- Rüdiger Bock
- Cong Yao
- Charles V. Stewart
- Andreas K. Maier
- M. Sofka
- Yonatan Wexler
In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
- Joachim Hornegger
- Georg Michelson
- Rüdiger Bock
- Charles V. Stewart
- Andreas K. Maier
- M. Sofka
- Yonatan Wexler
- Eyal Ofek
Citations in the corpus
- A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images (2016, background): “Manual segmentation of the vascular tree in retinal images is a tedious task that requires experience and skill [3], [7].”
- Robust Vessel Segmentation in Fundus Images (2013, methodology): “A more detailed review of the segmentation and other retinal image processing algorithms can be found in the articles published by Kirbas and Quek [2] and Patton et al. [3].”
- Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures (2006, methodology): “closely corresponds to the suite of methods that have been applied throughout the medical image analysis literature [41].”
- Detecting text in natural scenes with stroke width transform (2010, methodology): “In ultiscale pyramid of strategy; moreover, e detected using this mentation, see [ 13 ].”