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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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Updated: Dec 7, 2025

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

Frangi based multi-scale level sets for retinal vascular segmentation.

Jinzhu Yang¹, Mingxu Huang¹, Jie Fu²

¹Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Northeastern University, Shenyang, Liaoning 110169, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110169, China.

Computer Methods and Programs in Biomedicine

|September 24, 2020

Summary

This summary is machine-generated.

This study introduces a novel Frangi-based multi-scale level set method for segmenting retinal vessels in fundus images. The approach achieves state-of-the-art accuracy, overcoming challenges like image inhomogeneity and vessel thickness variations.

Keywords:

Hessian Level set Multi-scale Retinal vascular segmentation

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal vascular disease is a significant medical concern.
Accurate segmentation of retinal vessels from fundus images remains challenging due to image inhomogeneity and diverse vessel thicknesses.

Purpose of the Study:

To propose a novel method for segmenting retinal vessels from fundus images.
To address the limitations of existing segmentation techniques.

Main Methods:

Utilized a Frangi filter to enhance vascular structures and determine local optimal scales.
Developed multi-scale level set models incorporating the enhanced image and local optimal scales as inputs.
Evaluated the method on the DRIVE and STARE image repositories.

Main Results:

The proposed Frangi-based multi-scale level sets demonstrated effectiveness compared to scale-fixed versions.
Segmentation accuracy achieved was comparable to state-of-the-art methods on benchmark datasets.

Conclusions:

The proposed multi-scale level set models offer a robust solution for retinal vessel segmentation.
This method shows promise for improving the analysis of retinal vascular diseases.