Augmented Active Surface Model for the Recovery of Small Structures in CT

This paper devises an augmented active surface model for the recovery of small structures in a low resolution and high noise setting, where the role of regularization is especially important. The emphasis here is on evaluating performance using real clinical computed tomography (CT) data with compar...

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Bibliographic Details
Published in:IEEE transactions on image processing 2013-11, Vol.22 (11), p.4394-4406
Main Authors: Bradshaw, Andrew Philip, Taubman, David S., Todd, Michael J., Magnussen, John S., Halmagyi, G. Michael
Format: Article
Language:English
Subjects:
Active contour
Active contours
active surface
Algorithms
Applied sciences
B-spline
Computed tomography
Computer Simulation
Equations
Exact sciences and technology
ground truth
Humans
Image processing
Imaging, Three-Dimensional - methods
Information, signal and communications theory
Mathematical model
Models, Biological
Pattern recognition
Pattern Recognition, Automated - methods
point spread function
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
segmentation
semicircular canal
Sensitivity and Specificity
Signal processing
Smoothing methods
Splines (mathematics)
Telecommunications and information theory
Tomography, X-Ray Computed - methods
Vestibule, Labyrinth - diagnostic imaging
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Summary:This paper devises an augmented active surface model for the recovery of small structures in a low resolution and high noise setting, where the role of regularization is especially important. The emphasis here is on evaluating performance using real clinical computed tomography (CT) data with comparisons made to an objective ground truth acquired using micro-CT. In this paper, we show that the application of conventional active contour methods to small objects leads to non-optimal results because of the inherent properties of the energy terms and their interactions with one another. We show that the blind use of a gradient magnitude based energy performs poorly at these object scales and that the point spread function (PSF) is a critical factor that needs to be accounted for. We propose a new model that augments the external energy with prior knowledge by incorporating the PSF and the assumption of reasonably constant underlying CT numbers.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2013.2273666