Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation

By F. Zana, Jean-Claude Klein. IEEE Transactions on Image Processing, 2001

1,001 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/3e3601f777d0ad9de273cc2ef754e9c5043fd74a

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Citations in the corpus

A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images (2017, background): “The cross section of the retinal vessels is like the profile of a Gaussian function [13] and the intensity on the vessel’s centre line is lowest.”
Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs (2008, methodology): “Matched filters have also been widely used on retinal images to extract blood vessels in particular [16][17][18][19], usually Gaussian filters [16][19], sometimes morphological filters [20].”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, background): “Zana and Klein [10,47] combined morphological filters and crosscurvature evaluation to segment vessel-like patterns.”
DUNet: A deformable network for retinal vessel segmentation (2018, methodology): “Zana et al. presented an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment and they obtained an accuracy of 0.9377 [7].”
An improved matched filter for blood vessel detection of digital retinal images (2007, methodology): “Many methods, however, have been proposed for this purpose; matched ﬁlter [1], morphology edge detection [6], ridge-based vessel segmentation [7] which is called pixel classiﬁcation in [8], and wavelet transform [9].”
An effective retinal blood vessel segmentation method using multi-scale line detection (2013, methodology): “Jiang et al. [28] 0.9222 0.6915 – – Hoover et al. [17] – – 0.9240 0.6998 Perez et al. [30] 0.9316 0.7670 0.9196 0.7607 Zana et al. [27] 0.9320 0.7318 – – Chaudhuri et al. [16] 0.8884 0.5587 – – Ricci-line [8] 0.”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “This task is performed by means of morphological operations [22], matched ﬁlter responses [23]–[25], the complex continuous wavelet transform [26], among others [27].”
Hierarchical retinal blood vessel segmentation based on feature and ensemble learning (2015, methodology): “Mathematical morphology coupling curvature evaluation [8] and matched filtering for centerline detection [9,10] is also deployed for retinal vessel segmentation.”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “[8,56].”
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, background): “Mathematical morphology in combination with curvature evaluation [9] and matched filtering for centreline detection [10], [11] is also exploited for retinal vessel segmentation.”
FABC: Retinal Vessel Segmentation Using AdaBoost (2010, methodology): “Mathematical morphology and curvature evaluation are used also in [24] for the detection of vessel-like patterns.”
The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003, background): “Many techniques have been proposed in the research literature for vascular feature extraction [12, 38, 60, 72, 74, 86].”
Trainable COSFIRE filters for vessel delineation with application to retinal images (2015, methodology): “Other unsupervised methods, which use a priori information about the profile structure of the vessels, employ mathematical morphology to segment the vessel tree from the background (Zana and Klein, 2001; Heneghan et al., 2002; Mendonca and Campilho, 2006).”