Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images

This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour with...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2007-09, Vol.11 (5), p.537-543
Main Authors: Maroulis, D.E., Savelonas, M.A., Iakovidis, D.K., Karkanis, S.A., Dimitropoulos, N.
Format: Article
Language:English
Subjects:
Active contour models
Active contours
Artificial Intelligence
Biomedical imaging
Biomedical informatics
Cancer
Computed tomography
Computer Simulation
computer-aided diagnosis
Convergence
Delineation
Humans
Image edge detection
Image Interpretation, Computer-Assisted - methods
Inhomogeneities
Level set
level sets
Medical diagnostic imaging
Models, Biological
Nodules
Pattern Recognition, Automated - methods
Reproducibility of Results
Robustness
Sensitivity and Specificity
Shape
Thyroid Nodule - diagnostic imaging
thyroid nodules
Ultrasonic imaging
Ultrasonography
Ultrasonography - methods
Ultrasound
ultrasound (US)
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 computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable background regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2006.890018