A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was eval...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2011-01, Vol.30 (1), p.146-158
Main Authors: Marín, D, Aquino, A, Gegundez-Arias, M E, Bravo, J M
Format: Article
Language:English
Subjects:
Algorithms
Application software
Biomedical imaging
Blood vessels
Computer networks
Databases, Factual
Diabetes
Diabetic retinopathy
Diabetic Retinopathy - diagnosis
Eyes & eyesight
Fluorescein Angiography - methods
Humans
Image databases
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Information Storage and Retrieval - methods
moment invariants
Neural networks
Pattern Recognition, Automated - methods
Reproducibility of Results
Retina
retinal imaging
Retinal Vessels - anatomy & histology
Sensitivity and Specificity
Spatial databases
telemedicine
Testing
vessels segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2010.2064333