Algorithms for digital image processing in diabetic retinopathy
- PMID: 19616920
- DOI: 10.1016/j.compmedimag.2009.06.003
Algorithms for digital image processing in diabetic retinopathy
Abstract
This work examined recent literature on digital image processing in the field of diabetic retinopathy. Algorithms were categorized into 5 steps (preprocessing; localization and segmentation of the optic disk; segmentation of the retinal vasculature; localization of the macula and fovea; localization and segmentation of retinopathy). The variety of outcome measures, use of a gold standard or ground truth, data sample sizes and the use of image databases is discussed. It is intended that our classification of algorithms into a small number of categories, definition of terms and discussion of evolving techniques will provide guidance to algorithm designers for diabetic retinopathy.
Similar articles
-
Contextual detection of diabetic pathology in wide-field retinal angiograms.Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:5437-40. doi: 10.1109/IEMBS.2008.4650444. Annu Int Conf IEEE Eng Med Biol Soc. 2008. PMID: 19163947
-
Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.Comput Methods Programs Biomed. 2012 Aug;107(2):274-93. doi: 10.1016/j.cmpb.2011.06.007. Epub 2011 Jul 14. Comput Methods Programs Biomed. 2012. PMID: 21757250
-
Using a patient image archive to diagnose retinopathy.Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:5441-4. doi: 10.1109/IEMBS.2008.4650445. Annu Int Conf IEEE Eng Med Biol Soc. 2008. PMID: 19163948
-
Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter.IEEE Trans Med Imaging. 2008 Jan;27(1):11-8. doi: 10.1109/TMI.2007.900326. IEEE Trans Med Imaging. 2008. PMID: 18270057
-
Digital image processing for clinicians, part II: filtering.J Nucl Cardiol. 2002 Jul-Aug;9(4):429-37. doi: 10.1067/mnc.2002.122898. J Nucl Cardiol. 2002. PMID: 12161720 Review. No abstract available.
Cited by
-
Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix.Med Image Anal. 2014 Aug;18(6):903-13. doi: 10.1016/j.media.2013.09.009. Epub 2013 Oct 26. Med Image Anal. 2014. PMID: 24238743 Free PMC article.
-
QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.Retina. 2020 Feb;40(2):322-332. doi: 10.1097/IAE.0000000000002373. Retina. 2020. PMID: 31972803 Free PMC article.
-
Red-lesion extraction in retinal fundus images by directional intensity changes' analysis.Sci Rep. 2021 Sep 14;11(1):18223. doi: 10.1038/s41598-021-97649-x. Sci Rep. 2021. PMID: 34521886 Free PMC article.
-
HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation.PLoS One. 2021 Sep 7;16(9):e0257013. doi: 10.1371/journal.pone.0257013. eCollection 2021. PLoS One. 2021. PMID: 34492064 Free PMC article.
-
Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function.Healthcare (Basel). 2022 Dec 28;11(1):97. doi: 10.3390/healthcare11010097. Healthcare (Basel). 2022. PMID: 36611557 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical