Automatic Active Model Initialization via Poisson Inverse Gradient

Authors:
Bing Li;S. T. Acton
Affiliations:
Dept. of Electr. & Comput. Eng., Univ. of Virginia, Charlottesville, VA;-
Venue:
IEEE Transactions on Image Processing
Year:
2008

Citing 0
Cited 3

Left Ventricle Segmentation Using Model Fitting and Active Surfaces

Journal of Signal Processing Systems
Poisson inverse gradient approach to vascular myocyte detection and segmentation

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Segmentation of the left ventricle using active contour method with gradient vector flow forces in short-axis MRI

ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine

Quantified Score

Hi-index	0.01

Visualization

Abstract

Active models have been widely used in image processing applications. A crucial stage that affects the ultimate active model performance is initialization. This paper proposes a novel automatic initialization approach for parametric active models in both 2-D and 3-D. The PIG initialization method exploits a novel technique that essentially estimates the external energy field from the external force field and determines the most likely initial segmentation. Examples and comparisons with two state-of-the-art automatic initialization methods are presented to illustrate the advantages of this innovation, including the ability to choose the number of active models deployed, rapid convergence, accommodation of broken edges, superior noise robustness, and segmentation accuracy.