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Related Experiment Video

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Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Published on: May 19, 2023

Recent Advancements in Retinal Vessel Segmentation.

Chetan L Srinidhi¹, P Aparna², Jeny Rajan³

¹Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India. srinidhipy@gmail.com.

Journal of Medical Systems

|March 13, 2017

Summary

This review summarizes recent automated retinal vessel segmentation methods for ocular disease diagnosis. It analyzes preprocessing, techniques, performance metrics, and future directions for computer-aided diagnosis systems.

Keywords:

Fundus image Retinal vessel Review Segmentation

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Area of Science:

Medical Imaging
Ophthalmology
Computer Vision

Background:

Automated retinal vessel segmentation is crucial for diagnosing and planning treatment of ocular diseases.
Despite extensive research, accurate segmentation remains challenging due to image artifacts and anatomical variations.

Purpose of the Study:

To systematically review recent advancements in retinal vessel segmentation methods (last five years).
To discuss preprocessing steps, state-of-the-art techniques, performance metrics, and limitations.
To provide insights into future research directions for computer-aided diagnosis systems.

Main Methods:

Systematic literature review of retinal vessel segmentation techniques published in the last five years.
Classification of methods based on their underlying principles.
Quantitative analysis of performance metrics including sensitivity, specificity, and accuracy.

Main Results:

Identification and categorization of recent state-of-the-art retinal vessel segmentation techniques.
Analysis of the strengths and weaknesses of current segmentation approaches.
Discussion of emerging performance metrics and their relevance.

Conclusions:

Retinal vessel segmentation is a complex but vital task for ocular diagnostics.
Recent advancements show promise, but challenges remain in handling image variability and abnormalities.
Further research is needed to develop robust computer-aided diagnostic systems.