Hybrid Methodology For Retinal Vessel Segmentation Approach Based On Machine Learning
Manisha Deo, Animesh Dubey.
Abstract
Retinal vessel segmentation is an essential phase of diabetes detection. In detecting retinal vessels, image processing and machine learning play a vital role. Machine learning algorithms enhance the segmentation ratio of retinal images. This paper proposed machine learning-based retinal vessel segmentation methods. The proposed methods use wavelet transform methods to extract texture features and support vector machine applied for retinal vessel detection. The proposed algorithm improves the segmentation ratio of the retinal vessel. The lower content of the retinal image increases the range of the feature sample and maximizes the cover of the support vector. The proposed algorithm is simulated in MATLAB tools—the DRIVE dataset used to test the proposed algorithm.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
The articles in Bibliomed are open access articles licensed under Creative Commons Attribution 4.0 International License (CC BY), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
We use cookies and other tracking technologies to work properly, to analyze our website traffic, and to understand where our visitors are coming from. More InfoGot It!
