Segmentation-free and multiscale-free extraction of medial information using Gradient Vector Flow - Application to vascular structures

Gradient Vector Flow has become a popular method to recover medial information in medical imaging, in particular for vessels centerline extraction. This renewed interest has been motivated by its ability to process gray-scale images without prior segmentation. However, another interesting property l...

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Bibliographic Details
Main Authors: Pizaine, G., Prevost, R., Angelini, E. D., Bloch, I., Makram-Ebeid, S.
Format: Conference Proceeding
Language:English
Subjects:
diffusion
Equations
gradient vector flow
Image edge detection
Image segmentation
Mathematical model
medialness
Noise
shape analysis
Skeleton
Vectors
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Summary:Gradient Vector Flow has become a popular method to recover medial information in medical imaging, in particular for vessels centerline extraction. This renewed interest has been motivated by its ability to process gray-scale images without prior segmentation. However, another interesting property lies in the diffusion process used to solve the underlying variational problem. We propose a method to recover scale information in the context of vascular structures extraction, relying on analytical properties of the Gradient Vector Flow only, with no multiscale analysis. Through simple one-dimensional considerations, we demonstrate the ability of our approach to estimate the radii of the vessels with an error of 10% only in the presence of noise and less than 3% without noise. Our approach is evaluated on convolved bar-like templates and is illustrated on 2D X-ray angiographic images.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2012.6235533