Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation

Mistry, Kamlesh, Issac, Biju, Jacob, Seibu Mary, Jasekar, Jyoti and Zhang, Li (2018) Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, pp. 424-429. ISBN 978-1-5386-9583-8

[img] Text
Mistry et al - Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation AAM.pdf - Accepted Version
Restricted to Repository staff only

Download (559kB)
Official URL: http://dx.doi.org/10.1109/ICARCV.2018.8581322

Abstract

The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity.

Item Type: Book Section
Subjects: B900 Others in Subjects allied to Medicine
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 23 Jan 2019 08:46
Last Modified: 23 Oct 2019 13:45
URI: http://nrl.northumbria.ac.uk/id/eprint/37691

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics