Automated Thickness Measurement of Retinal Blood Vessels for Implementation of Clinical Decision Support Systems in Diagnostic Diabetic Retinopathy
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..
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:52248", author = "S.Jerald Jeba Kumar and M.Madheswaran", title = "Automated Thickness Measurement of Retinal Blood Vessels for Implementation of Clinical Decision Support Systems in Diagnostic Diabetic Retinopathy", 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.", volume = "4", number = "4", pages = "123-5", }