Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database

By J. Odstrčilík, R. Kolář, Attila Budai, Joachim Hornegger, Jirí Jan, J. Gazárek, T. Kubena, P. Cernosek, Ondřej Svoboda, E. Angelopoulou. IET Image Processing, 2013

461 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/24673d4e3559fd1bdeef0409944f7f2f9e4a720d

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

1. Erik J. Bekkers
2. Andreas K. Maier
3. José Ignacio Orlando
4. B. T. H. Romeny
5. Joachim Hornegger
6. Matthew B. Blaschko
7. Georg Michelson
8. Bowei Feng

In Ophthalmology

1. José Ignacio Orlando
2. Joachim Hornegger
3. Matthew B. Blaschko
4. Remco Duits
5. Georg Michelson
6. Erik J. Bekkers
7. Rüdiger Bock
8. Josien P. W. Pluim

In Radiology, Nuclear Medicine and Imaging

1. Andreas K. Maier
2. José Ignacio Orlando
3. Joachim Hornegger
4. Matthew B. Blaschko
5. Remco Duits
6. Georg Michelson
7. Erik J. Bekkers
8. Rüdiger Bock

Citations in the corpus

• Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, methodology): “In this work, four established publicly available color retinal fundus datasets DRIVE [28], STARE [7], CHASE_DB1 [30] and the High-Resolution Fundus dataset HRF [31] and our two newly public IOSTAR [24] and RC-SLO [25] (RetinaCheck Scanning Laser Ophthalmoscopy) datasets are used for evaluating the ”
• A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “Our experiments were carried out on DRIVE [44], STARE [46], CHASEDB1 [12], and HRF [24], [47], four standard publicly available datasets of fundus images used for the evaluation of blood vessel segmentation algorithms.”
• Computer Methods and Programs in Biomedicine (unknown): “[68] 40 CFP ARIA [137] 143 CFP CHASE [138] 28 CFP HRF [139] 45 CFP IMAGERET [140] 219 CFP MESSIDOR 1200 CFP ( http://messidor.crihan.fr ) REVIEW [141] 16 CFP ROC [142] 100 CFP VICAVR 58 CFP ( http://www.varpa.es ) VAMPIRE 8 FA ( http://vampire.computing . dundee.ac.uk) CASDQEF [143] Coronary 48 CTA…”
• Robust Vessel Segmentation in Fundus Images (2013, methodology): “Since high resolution images are becoming more common in clinical use, we evaluated our methods on the high resolution ( 3504 × 2336 pixels) images available [33], which were already used to evaluate other methods [6, 7].”
• A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images (2016, methodology): “For high-resolution datasets, such as HRF [35], the vessel width can be 30 pixels or larger.”