Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 24, 2026

Doppler Optical Coherence Tomography of Retinal Circulation

Doppler Optical Coherence Tomography of Retinal Circulation

Published on: September 18, 2012

Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis.

Nittaya Muangnak¹, Pakinee Aimmanee², Stanislav Makhanov¹

¹Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathum Thani, 12000, Thailand.

Medical & Biological Engineering & Computing

|August 25, 2017

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble.

PloS one·2024

Same author

Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.

PloS one·2022

Same author

Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images.

Medical & biological engineering & computing·2022

See all related articles

New algorithms for optic disk (OD) detection use retinal blood vessel analysis for high accuracy, even in poor quality images. The hybrid method (HM) achieves 98% OD localization accuracy and shows promise for detecting diabetic retinopathy (DR).

Area of Science:

Ophthalmology and Medical Imaging
Biomedical Engineering
Computer Vision

Background:

Accurate detection and segmentation of the optic disk (OD) are crucial for diagnosing various eye conditions.
Existing methods often struggle with image quality variations, particularly in retinal fundus images.
Retinal blood vessel patterns offer valuable information for robust OD localization and segmentation.

Purpose of the Study:

To develop and evaluate novel algorithms for automatic optic disk (OD) detection and segmentation.
To improve OD detection accuracy, especially in low-quality retinal images.
To assess the clinical utility of the developed methods for detecting diabetic retinopathy (DR).

Main Methods:

Vessel vector-based phase portrait analysis (VVPPA) utilizing retinal blood vessel direction.

Keywords:

Hybrid approach Optic disk detection Optic disk localization Smart phone retinal camera Vessel-based phase portrait analysis

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

Related Experiment Videos

Last Updated: Feb 24, 2026

Doppler Optical Coherence Tomography of Retinal Circulation

Doppler Optical Coherence Tomography of Retinal Circulation

Published on: September 18, 2012

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

A hybrid method (HM) combining VVPPA with the vessel transform (VT) for enhanced OD localization.

Integration of scale space (SS) with VVPPA and HM for OD contour identification (SSVVPPAC, SSHMC).

Main Results:

The hybrid method (HM) achieved 98% accuracy in optic disk localization, outperforming benchmark methods, particularly on poor-quality images.
The SSHMC segmentation method demonstrated superior performance, achieving high positive predictive value (PPV) and sensitivity for poor-quality images.
In a clinical setting, the HM demonstrated high efficacy in detecting diabetic retinopathy (DR) abnormalities, with 98.13% true positive rate.

Conclusions:

The proposed VVPPA and HM algorithms offer a robust and accurate approach to optic disk detection and segmentation.
These methods are particularly effective in challenging low-quality retinal images, enhancing diagnostic capabilities.
The HM shows significant potential for clinical applications, including the automated detection of diabetic retinopathy using smartphone-based imaging.