Retinal hemorrhage detection by rule-based and machine learning approach

Di Xiao, Shuang Yu, Janardhan Vignarajan, Dong An, Mei-Ling Tay-Kearney, Yogi Kanagasingam

Research output: Chapter in Book/Conference paperConference paperpeer-review

19 Citations (Scopus)

Abstract

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of PublicationSouth Korea
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages660-663
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - 13 Sept 2017
Event2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Abbreviated titleEMBC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17

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