Show simple item record

dc.contributor.authorChalloob, Mohsin
dc.contributor.authorGao, Yongsheng
dc.contributor.editorFelsberg, M
dc.contributor.editorHeyden, A
dc.contributor.editorKruger, N
dc.description.abstractThe matched filter is an effective method for the detection of retinal vessels when combined with other processing techniques. This paper presents a segmentation method to improve the extraction of retinal vessels based on the matched filter. The method combines a morphological approach to enhance retinal vessels before applying the matched filter and a modified joint relative entropy (MJRE) thresholding method to segment the matched filter response. The morphological approach is designed to suppress irregular bright regions and noise while preserving the information of vessel edges, and to improve the contrast of vessels, especially thin ones. The joint relative entropy thresholding is modified to provide an optimal threshold value for segmenting the retinal vessel tree properly. The proposed method is tested on the DRIVE dataset, yielding an average accuracy, specificity and sensitivity of 0.9546, 0.9742 and 0.7527 respectively. Experimental results demonstrate that the proposed method achieved better performance than the state-of-the-art methods.
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.titleRetinal vessel segmentation using matched filter with joint relative entropy
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.hasfulltextNo Full Text
gro.griffith.authorGao, Yongsheng
gro.griffith.authorChalloob, Mohsin

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record