Automated Thickness Measurement of Retinal Blood Vessels for Implementation of Clinical Decision Support Systems in Diagnostic Diabetic Retinopathy
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Automated Thickness Measurement of Retinal Blood Vessels for Implementation of Clinical Decision Support Systems in Diagnostic Diabetic Retinopathy

Authors: S.Jerald Jeba Kumar, M.Madheswaran

Abstract:

The structure of retinal vessels is a prominent feature, that reveals information on the state of disease that are reflected in the form of measurable abnormalities in thickness and colour. Vascular structures of retina, for implementation of clinical diabetic retinopathy decision making system is presented in this paper. Retinal Vascular structure is with thin blood vessel, whose accuracy is highly dependent upon the vessel segmentation. In this paper the blood vessel thickness is automatically detected using preprocessing techniques and vessel segmentation algorithm. First the capture image is binarized to get the blood vessel structure clearly, then it is skeletonised to get the overall structure of all the terminal and branching nodes of the blood vessels. By identifying the terminal node and the branching points automatically, the main and branching blood vessel thickness is estimated. Results are presented and compared with those provided by clinical classification on 50 vessels collected from Bejan Singh Eye hospital..

Keywords: Diabetic retinopathy, Binarization, SegmentationClinical Decision Support Systems.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058546

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1994

References:


[1] Hind Azegrouz, Emanuele Trucco, Baliean Dhilon, "Thickness dependent tortuosity estimation for retinal blood vessels" Thomas Mac Gillivray and I.J. Mac Cormick, 2008.
[2] Y N. Patton, T.M.Aslam. T.MacGillivray. I.J.Deary.B.Dhillon, "Retinal image analysis:concepts, applications, and potential" Prog. Rtin. Eye Res. R. Vol.25 no. 1, pp. 99-127, 2006.
[3] N.Patton, Maini R, T.MacGillivray T.M.Aslam, I.J.Deary. B.Dhillon B."Effect of axial length on retinal vascular network geometry," Amer.Journ. Opthalmol.vol.140 no. pp.648-53, 2005.
[4] W.Hart and M.Goldbaum and B.Cote and P.KIube and M.Nelson"Measurement and classification of retinal vascular torturoity". Int.Journ. Medical Informatics, Vol. 53 No.2-3 pp. 239-252, February 1999.
[5] C.Heneghanm J. Flaynn, M. Okeefe, M.Cahill, "Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis". in Medical image Analysis, Vol, 6, issue 4,pp.407- 429 , Dec 2002.
[6] E. Graisan, M. Foracchia, A.Ruggeri, "A novel method for the automatic evaluation of retinal vessel tortuosity". Proc. 25th IEEE EMBS, pp. 86- 869, Sep 2003.
[7] E.Bullitt, G.Gerig, S.Prizer, W.Lin and S.Aylward, "Measuring Tortuosity of the Interacerebral vasculature from MRA images" IEEE Trans. Med. Imag., Vol. 22, pp. 1163-1171,2003.