Automatic Retinal Blood Vessel Segmentation Based on Multi-Level Convolutional Neural Network

Jinnan Guo, Shiwei Ren*, Yueting Shi, Haoyu Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, the automatic retinal blood segmentation method is essential for computer-aided diagnosis system. In this paper, a supervised method which is based on multi-level convolutional neural network is proposed to separate blood vessels from fundus image. By using both local and global feature extractors, small vessels can be well distinguished and global spatial consistency of the image can be ensured. Meanwhile, unsupervised pre-processing and postprocessing methods are applied to achieve better segmentation results. Experiment results on public database show that the proposed method outperforms the state-of-the-art performance (AUC up to >0.978) on DRIVE database.

Original languageEnglish
Title of host publicationProceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
EditorsWei Li, Qingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538676042
DOIs
Publication statusPublished - 2 Jul 2018
Event11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 - Beijing, China
Duration: 13 Oct 201815 Oct 2018

Publication series

NameProceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018

Conference

Conference11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
Country/TerritoryChina
CityBeijing
Period13/10/1815/10/18

Keywords

  • Convolutional neural network
  • computer-aided diagnosis
  • deep learning
  • retinal vessel segmentation

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