Segmentation of retinal blood vessels using the radial projection and semi-supervised approach

By Xinge You, Qinmu Peng, Yuan Yuan, Yiu-ming Cheung, Jiajia Lei. Pattern Recognition, 2011

401 citations recorded by the source corpus.

Automatic segmentation of retinal blood vessels has become a necessary diagnostic procedure in ophthalmology. The blood vessels consist of two types of vessels, i.e., thin vessels and wide vessels. Therefore, a segmentation method may require two different processes to treat different vessels. However, traditional segmentation algorithms hardly draw a distinction between thin and wide vessels, but deal with them together. The major problems of these methods are as follows: (1) If more emphasis is placed on the extraction of thin vessels, the wide vessels tend to be over detected; and more artificial vessels are generated, too. (2) If more attention is paid on the wide vessels, the thin and low contrast vessels are likely to be missing. To overcome these problems, a novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method is presented in this paper. The radial projection method is used to locate the vessel centerlines which include the low-contrast and narrow vessels. Further, we modify the steerable complex wavelet to provide better capability of enhancing vessels under different scales, and construct the vector feature to represent the vessel pixel by line strength. Then, semi-supervised self-training is used for extraction of the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels. Our approach is tested on two publicly available databases. Experiment results show that the method can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/247d14b87f46f995966113e472e2177a70c90487

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In Computer Vision and Pattern Recognition

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “Besides, the k-nearest neighbor classifier (KNN) [23] and the support vector machine (SVM) [24] have also been studied extensively for retinal vessel segmentation.”
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, neutral): “vector machine (SVM) [21].”
Semi-supervised Medical Image Segmentation through Dual-task Consistency (2020, methodology): “Semi-Supervised Medical Image Segmentation: For semi-supervised medical image segmentation, traditional methods mainly use hand-crafted features to design a model to perform segmentation, which includes the prior-based models (You et al. 2011) and the clustering-based models (Portela, Cavalcanti, an”
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images (2015, methodology): “We chose the most recent seven supervised methods [7], [8], [17], [18], [21]–[23].”
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.”
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, methodology): “You et al. [25] computed the feature vector by using the steerable complex wavelet followed by calculating the line strength [22]; the SVM is used for semisupervised classification.”
DUNet: A deformable network for retinal vessel segmentation (2018, neutral): “Various classiﬁers such as k-NN classiﬁer [19], support vector machine (SVM) [20] [21], artiﬁcial neural networks (ANN) [22], AdaBoost [23] etc, have been proposed to deal with different tasks.”
An effective retinal blood vessel segmentation method using multi-scale line detection (2013, methodology): “A supervised classifier (ranging from the use of artificial neural networks [21], kNN [6] [22], support vector machines [8] [26], Bayesian classifier [7], etc.) is used to train the model and classify each image pixel as vessel or background.”