Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction

By Ana Maria Mendonça, Aurélio J. C. Campilho. IEEE Transactions on Medical Imaging, 2006

1,021 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/30cce883b1964c9fbde9cc8f038372b9b69f4490

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images (2015, methodology): “We selected nine unsupervised segmentation methods [5], [6], [11], [12], [15], [41]–[43].”
Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction (2011, background): “Furthermore, the network of retinal vessels is distinctive enough to each individual and can be used for biometric identification, although it has not yet been extensively explored [3].”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “Mendonca and Campilho [58] DRIVE 0.”
Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016, methodology): “[19] proposed differential filters in combination with morphological operators to detect vessel centerlines.”
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.”
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.”
Robust Vessel Segmentation in Fundus Images (2013, neutral): “…were further tested on the three datasets of our own public [24] 0.718 0.974 0.941 Soares et al. [35] 0.7283 0.9788 0.9466 Mendonc¸a and Campilho [36] 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.…”
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).”
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database (2013, methodology): “In this way, prior knowledge of the vessel shapes is utilised to create a morphological structuring element, which is used for ﬁ ltering objects from background [27].”
Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement (2012, background): “The effectiveness of morphological, rather than linear, filters for vessel detection has also been explored [22].”
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification (2007, methodology): “A similar approach is adopted in [17] where the orientation spans from 0 to 170 at steps of 10 , and in [13] where only four orientations are used.”
Multi-retinal disease classification by reduced deep learning features (2017, methodology): “Table 1 shows the performance analysis of various methods such as morphological reconstruction (MR) method [28], support vector machine (SVM) [24], and proposed method dimension reduced deep learning (DRDL) in which the sensitivity, specificity, and accuracy are compared.”
Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification (2015, methodology): “Next, 12 linear structuring elements each of length 15 pixels and 1 pixel width and angles incremented by 15 • from 0 through 180 • are used to generate tophat reconstructions of R [15], [10].”
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features (2011, methodology): “Other methods use mathematical morphology [15], [34]–[36] to benefit from a priori-known vasculature shape features, such as being piecewise linear and connected.”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, background): “The detailed procedure of evaluation of derivative sign and average derivative values is available in [15].”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “This approach has been applied several times in the literature [10], [43], and an example of the resulting image is illustrated in Fig.”
FABC: Retinal Vessel Segmentation Using AdaBoost (2010, methodology): “In a recent approach [3], the retinal vascular network is detected automatically by first extracting vessel centerlines using differential filters, and then, applying morphological operators for filling vessel segments.”