Retinal vessel profiling based on four piecewise Gaussian model

Zhiyang Ma; Huiqi Li

doi:10.1109/ICDSP.2015.7252048

Retinal vessel profiling based on four piecewise Gaussian model

Zhiyang Ma, Huiqi Li

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Retinal vessel profiling in ocular fundus images is a basic tool to measure and detect retinal vascular structure, which can provide important information to clinical diagnosis and medical research. In this paper, a four-piecewise Gaussian model is proposed to describe the intensity distribution of retinal vascular cross-section. Eight hundred seventy three vessel profiles from 34 color fundus images were tested and best-fit parameters were obtained using Levenberg-Marquardt method. The mean squared error of the four-piecewise Gaussian model is about 0.18. The proposed model can describe the vessel profile with or without central reflex, and the profile with non-uniform background well.

Original language	English
Title of host publication	2015 IEEE International Conference on Digital Signal Processing, DSP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1094-1097
Number of pages	4
ISBN (Electronic)	9781479980581, 9781479980581
DOIs	https://doi.org/10.1109/ICDSP.2015.7252048
Publication status	Published - 9 Sept 2015
Event	IEEE International Conference on Digital Signal Processing, DSP 2015 - Singapore, Singapore Duration: 21 Jul 2015 → 24 Jul 2015

Publication series

Name	International Conference on Digital Signal Processing, DSP
Volume	2015-September

Conference

Conference	IEEE International Conference on Digital Signal Processing, DSP 2015
Country/Territory	Singapore
City	Singapore
Period	21/07/15 → 24/07/15

Keywords

four piecewise Gaussian model
retinal image
retinal vessel profile

Access to Document

10.1109/ICDSP.2015.7252048

Cite this

Ma, Z., & Li, H. (2015). Retinal vessel profiling based on four piecewise Gaussian model. In 2015 IEEE International Conference on Digital Signal Processing, DSP 2015 (pp. 1094-1097). Article 7252048 (International Conference on Digital Signal Processing, DSP; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDSP.2015.7252048

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title = "Retinal vessel profiling based on four piecewise Gaussian model",

abstract = "Retinal vessel profiling in ocular fundus images is a basic tool to measure and detect retinal vascular structure, which can provide important information to clinical diagnosis and medical research. In this paper, a four-piecewise Gaussian model is proposed to describe the intensity distribution of retinal vascular cross-section. Eight hundred seventy three vessel profiles from 34 color fundus images were tested and best-fit parameters were obtained using Levenberg-Marquardt method. The mean squared error of the four-piecewise Gaussian model is about 0.18. The proposed model can describe the vessel profile with or without central reflex, and the profile with non-uniform background well.",

keywords = "four piecewise Gaussian model, retinal image, retinal vessel profile",

author = "Zhiyang Ma and Huiqi Li",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Digital Signal Processing, DSP 2015 ; Conference date: 21-07-2015 Through 24-07-2015",

year = "2015",

month = sep,

day = "9",

doi = "10.1109/ICDSP.2015.7252048",

language = "English",

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Ma, Z & Li, H 2015, Retinal vessel profiling based on four piecewise Gaussian model. in 2015 IEEE International Conference on Digital Signal Processing, DSP 2015., 7252048, International Conference on Digital Signal Processing, DSP, vol. 2015-September, Institute of Electrical and Electronics Engineers Inc., pp. 1094-1097, IEEE International Conference on Digital Signal Processing, DSP 2015, Singapore, Singapore, 21/07/15. https://doi.org/10.1109/ICDSP.2015.7252048

Retinal vessel profiling based on four piecewise Gaussian model. / Ma, Zhiyang; Li, Huiqi.
2015 IEEE International Conference on Digital Signal Processing, DSP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1094-1097 7252048 (International Conference on Digital Signal Processing, DSP; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Retinal vessel profiling in ocular fundus images is a basic tool to measure and detect retinal vascular structure, which can provide important information to clinical diagnosis and medical research. In this paper, a four-piecewise Gaussian model is proposed to describe the intensity distribution of retinal vascular cross-section. Eight hundred seventy three vessel profiles from 34 color fundus images were tested and best-fit parameters were obtained using Levenberg-Marquardt method. The mean squared error of the four-piecewise Gaussian model is about 0.18. The proposed model can describe the vessel profile with or without central reflex, and the profile with non-uniform background well.

AB - Retinal vessel profiling in ocular fundus images is a basic tool to measure and detect retinal vascular structure, which can provide important information to clinical diagnosis and medical research. In this paper, a four-piecewise Gaussian model is proposed to describe the intensity distribution of retinal vascular cross-section. Eight hundred seventy three vessel profiles from 34 color fundus images were tested and best-fit parameters were obtained using Levenberg-Marquardt method. The mean squared error of the four-piecewise Gaussian model is about 0.18. The proposed model can describe the vessel profile with or without central reflex, and the profile with non-uniform background well.

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BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015

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T2 - IEEE International Conference on Digital Signal Processing, DSP 2015

Y2 - 21 July 2015 through 24 July 2015

ER -

Retinal vessel profiling based on four piecewise Gaussian model

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