RMIT University
Browse

Adaptive Higuchi's dimension-based retinal vessel diameter measurement

conference contribution
posted on 2024-10-31, 20:00 authored by Behzad Aliahmad, Dinesh KumarDinesh Kumar
This paper proposes the use of an adaptive model to measure the width of retinal vessels in fundus photographs. It is based on the hypothesis that the Higuchi's dimension of the cross-section is proportional to the vessel diameter. This approach does not require image segmentation and binarization of the vessels and therefore is suitable for machine based measurements. The model is developed using a synthetic image of the vessel of known diameter and added noise. As a first step, the vessel cross-section profiles were manually obtained and analyzed using Higuchi's fractal dimension method. A 3D model was formed using the relationship between Higuchi's dimension, vessel diameter and noise variance. This model was then evaluated using expert annotated REVIEW public database and solved for the vessel diameter for performance evaluation. The result showed a good agreement with state of the art techniques. The model is background noise tolerant, estimates the vessel width with subpixel precision and does not require manual intervention.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1109/EMBC.2016.7590947
  2. 2.
    ISBN - Is published in 9781457702204 (urn:isbn:9781457702204)

Start page

1308

End page

1311

Total pages

4

Outlet

Proceedings of the IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC 2016)

Name of conference

EMBC 2016: Empowering Individual Healthcare Decisions through Technology

Publisher

IEEE

Place published

United States

Start date

2016-08-16

End date

2016-08-20

Language

English

Copyright

© IEEE 2016

Former Identifier

2006067554

Esploro creation date

2020-06-22

Fedora creation date

2016-10-26

Usage metrics

    Scholarly Works

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC