2017
DOI: 10.1016/j.patcog.2017.04.008
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Retinal vessel delineation using a brain-inspired wavelet transform and random forest

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Cited by 105 publications
(40 citation statements)
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“…Their AUC values are 0.9682 and 0.9789 for DRIVE and STARE datasets, respectively. Multiple scales and orientation features from the wavelet transform along with random forest classifier has improved performance metrics and achieves better AUC values [36].…”
Section: Quantitative Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Their AUC values are 0.9682 and 0.9789 for DRIVE and STARE datasets, respectively. Multiple scales and orientation features from the wavelet transform along with random forest classifier has improved performance metrics and achieves better AUC values [36].…”
Section: Quantitative Analysismentioning
confidence: 99%
“…Although unsupervised methods do not need prior knowledge about the segmentation and are fast to run [30,31], it takes time to interpret the results correctly. Supervised methods require a lot of features and expertise to accurately segment the blood vessels [32][33][34][35][36][37]. Supervised learning algorithms based on deep convolutional neural networks (CNN) show utmost robustness and efficiency in segmenting the blood vessels.…”
Section: Introductionmentioning
confidence: 99%
“…Orlando et al proposed a fully connected conditional random field model, using a structured output support vector machine to learn model parameters, and performed retinal vessel segmentation [ 15 ]. Zhang et al extracted the features by vessel filtering and wavelet transform strategy, applied the random forest training strategy learn the classifier’s parameters, and performed retinal vessel segmentation [ 16 ]. For the traditional machine learning methods, feature selection has great influence on segmentation accuracy, and the independent features with high vessel recognition rate is the critical step.…”
Section: Introductionmentioning
confidence: 99%
“…In general, the corneal nerve fiber segmentation can be considered as a problem within the scope of curvilinear structure enhancement and segmentation tasks [22][23][24]. For instance, extensive conventional approaches have been applied to segment elongated medical imaging structures in three categories: classifier based, tracking based and filter based.…”
Section: Introductionmentioning
confidence: 99%