Instance-Based Learning for Blood Vessel Segmentation in Retinal Images

2019 ◽  
pp. 111-118
Author(s):  
Worapan Kusakunniran ◽  
Sarattha Kanchanapreechakorn ◽  
Kittikhun Thongkanchorn
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Retinal Images ◽  
Blood Vessel Segmentation ◽  
Instance Based Learning

scholarly journals Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1067
Author(s):  
Dali Chen ◽  
Yingying Ao ◽  
Shixin Liu
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Supervised Learning ◽  
Vessel Segmentation ◽  
Training Dataset ◽  
Retinal Images ◽  
Learning Method ◽  
Learning Network ◽  
Blood Vessel Segmentation ◽  
Deep Learning Network

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

2017 ◽  
Vol 7 (3) ◽  
pp. 1414
Author(s):  
Fauziah Kasmin ◽  
Azizi Abdullah ◽  
Anton Satria Prabuwono
Keyword(s):  
Blood Vessel ◽  
Vessel Segmentation ◽  
Support Vector ◽  
Retinal Images ◽  
Blood Vessel Segmentation ◽  
Training Images ◽  
Retinal Blood Vessels ◽  
Average Accuracy ◽  
Long Time ◽  
Automated Screening

This paper describes a method on segmentation of blood vessel in retinal images using supervised approach. Blood vessel segmentation in retinal images can be used for analyses in diabetic retinopathy automated screening. It is a very exhausting job and took a very long time to segment retinal blood vessels manually. Moreover these tasks also requires training and skills. The strategy involves the applications of Support Vector Machine to classify each pixel whether it belongs to a vessel or not. Single mask filters which consist of intensity values of normalized green channel have been generated according to the direction of angles. These single oriented mask filters contain the vectors of the neighbourhood of each pixel. Five images randomly selected from DRIVE database are used to train the classifier. Every single oriented mask filters are ranked according to the average accuracy of training images and their weights are assigned based on this rank.  Ensemble approaches that are Addition With Weight and Product With Weight have been used to combine all these single mask filters. In order to test the proposed approach, two standard databases, DRIVE and STARE have been used. The results of the proposed method clearly show improvement compared to other single oriented mask filters.

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