Segmentation of blood vessels from red-free and fluorescein retinal images

By M. Elena Martínez-Pérez, Alun D. Hughes, Simon A. Thom, Anil Anthony Bharath, Kim H. Parker. Medical Image Anal., 2007

471 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a7f449363ef5ed272df02a2d27c9ed98393c19c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features (2011, methodology): “Other rule-based methods for retinal blood vessel segmentation were reported in [46] and [47].”
DUNet: A deformable network for retinal vessel segmentation (2018, methodology): “proposed a method to automatically segment retinal blood vessels based on multiscale feature extraction [9].”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, unknown): “[11] applied a”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “[72] 2007 Maximum principal curvature, gradient magnitude and region growing TPR, FPR, Acc No Perez et al.”
Robust Vessel Segmentation in Fundus Images (2013, unknown): “…0.7344 0.9764 0.9452 Staal et al. [37] 0.7194 0.9773 0.9442 Niemeijer et al. [38] — — 0.9416 Zana and Klein [39] — — 0.9377 Martinez-Perez et al. [40] 0.7246 0.9655 0.9344 Odstr ̌ cil´ık et al. [7] 0.7060 0.9693 0.9340 Espona et al. (subpixel accuracy) [41] 0.7313 0.9600 0.9325 Chaudhuri et al.…”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, background): “[26], which has reported a higher Sp value but a lower average Se.”
An effective retinal blood vessel segmentation method using multi-scale line detection (2013, methodology): “In [30], the local maxima over scales of the gradient magnitude and the maximum principle curvature are used as two features to classify each pixel as vessel or background using multiple pass region growing process.”