Statistical-based linear vessel structure detection in medical images

Authors:
Mouloud Adel;Monique Rasigni;Thierry Gaidon;Caroline Fossati;Salah Bourennane
Affiliations:
Institut FRESNEL, UMR, CNRS, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Marseille Cedex, France;Institut FRESNEL, UMR, CNRS, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Marseille Cedex, France;Institut FRESNEL, UMR, CNRS, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Marseille Cedex, France;Institut FRESNEL, UMR, CNRS, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Marseille Cedex, France;Institut FRESNEL, UMR, CNRS, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Marseille Cedex, France
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
Cited 0

Bayesian detection of the fovea in eye fundus angiographies

Pattern Recognition Letters
Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of vessel extraction techniques and algorithms

ACM Computing Surveys (CSUR)
Non-Linear Model Fitting to Parameterize Diseased Blood Vessels

VIS '04 Proceedings of the conference on Visualization '04
Model-based quantitative AAA image analysis using a priori knowledge

Computer Methods and Programs in Biomedicine
Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

IEEE Transactions on Information Technology in Biomedicine
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Linear structures such as blood vessels in medical images are important features for computer-aided diagnosis and follow-up of many diseases. In this letter a new tracking-based segmentation method is proposed to detect blood vessels in retinal angiorams. Bayesian segmentation with the Maximum a posteriori (MAP) Probability criterion is used for that purpose. First promising results are presented and discussed.