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Using Retinal Imaging to Study Dementia
Published on: November 6, 2017
Automatic localization of the optic disc by combining vascular and intensity information
Ana Maria Mendonça1, António Sousa, Luís Mendonça
1INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal; FEUP - Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
Summary
This study introduces a novel method for automatically locating the optic disc (OD) in retinal images by analyzing blood vessel patterns and image intensity. The approach demonstrates high accuracy, successfully identifying the optic disc in nearly all tested images.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Accurate localization of the optic disc (OD) is crucial for diagnosing various eye diseases.
- Existing methods for automatic OD detection often struggle with variations in image quality and anatomical structures.
Purpose of the Study:
- To develop and validate a robust methodology for the automatic location of the optic disc in retinal images.
- To improve the accuracy and reliability of optic disc localization using a combination of vascular and intensity information.
Main Methods:
- A novel approach combining blood vessel network information with image intensity data for optic disc localization.
- Quantification of vessel orientation distribution using the entropy of vascular directions.
- Constraining the search for maximal entropy to high-intensity image regions to enhance robustness.
Main Results:
- The proposed method achieved a high success rate, accurately locating the optic disc in 1357 out of 1361 images across four diverse datasets.
- The integration of vascular orientation entropy and intensity-based constraints significantly improved localization accuracy.
Conclusions:
- The developed methodology offers a reliable and accurate solution for automatic optic disc localization in retinal imaging.
- This technique has the potential to aid in the early detection and monitoring of optic nerve disorders.
