FABC: Retinal Vessel Segmentation Using AdaBoost

By Carmen Alina Lupascu, Domenico Tegolo, Emanuele Trucco. IEEE Transactions on Information Technology in Biomedicine, 2010

368 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/3474cb97cb08469f1e9d71bfcc284c4b300310af

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In Computer Vision and Pattern Recognition

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images (2016, methodology): “However, in the Lupascu [34] and Fraz [8] methods, less than 10% of the total pixels are used as training samples.”
Hierarchical retinal blood vessel segmentation based on feature and ensemble learning (2015, methodology): “[22] used a feature vector at different spatial scales for each pixel.”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “[37] 2010 Feature based AdaBoost classifier SN, Acc, AUC No You et al.”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, methodology): “These methods include neural networks [24], back propagation neural network [25], principal component analysis [26], k-nearest neighbor classifiers [27], ridge based primitives classification [14], Gabor wavelet with supervised classification using a mixture of Gaussian [28], line operators and supp”
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images (2015, background): “, k-nearest neighbors [17], support vector machine (SVM) [18], [19], artificial neural networks (ANN) [20], Gaussian mixture models (GMM) [8], [21], AdaBoost [22], or conditional random fields (CRFs) [23]) so that it can be used for the classification of image pixels as either vessel or not in a new”
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, methodology): “[23] introduced a feature-based Ada–Boost classifier for vessel segmentation which utilizes a 41-D feature vector at different spatial scales for each pixel.”
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database (2013, neutral): “[21].”
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “[20] adopted an AdaBoost classifier based on simple feature vectors consist-”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, methodology): “[23] constructed the feature vector consisting of local intensity, spatial properties and geometry and adopted an AdaBoost classifier for segmentation.”
Computer Methods and Programs in Biomedicine (neutral): “In [65] , the feature-based AdaBoost classiﬁer is used to segment retinal vessels.”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, neutral): “Several classiﬁers have been considered in the literature, including k -nearest neighbors [6], Bayesian [7], support vector machines (SVMs) [8], [9], neural networks [10], [11], decision trees [12], [13], Gaussian mixture models [5], AdaBoost [14], among others.”