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Journal of Medical Systems

Erschienen in:

01.06.2008 | Original Paper

Wavelet-based Multi-resolution Deformation for Medical Endoscopic Image Segmentation

verfasst von: Sung Won Yoon, Chungkeun Lee, Jin Kwon Kim, Myoungho Lee

Erschienen in: Journal of Medical Systems | Ausgabe 3/2008

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Abstract

Image segmentation is an essential technique in image analysis. In spite of issues in contour initialization, boundary concavities and high-level computation, active contour models (snakes) are popular and successful method for segmentation among researchers. Segmentation process in snakes consists of calculation of energy and deformation of contour. In this paper, we present a new deformation method for active contour model, multi-resolution deformation based on wavelet ensuring powerful time reduction, high accuracy supported by stable results in convergence of an initial contour to target boundary in medical image segmentation.

Kass, M., Witkin, A. P., and Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis 1:4321–331, 1987, Jan.CrossRef

Xu C., and Prince, J. L., Snakes shapes and gradient vector flow. IEEE Trans. Image Process. 7:3359–369, 1998, Mar.MATHCrossRefMathSciNet

Leroy, B., Herlin, I., and Cohen, L. D., Multi-resolution algorithms for active contour models. Proc. 12th Int. Conf. on Analysis and Optimization of Systems, Paris, France, 58–65, June, 1996.

Cohen, L. D., On active contour models and balloons. CVGIP: Image Underst 53:2211–218, 1991.MATHCrossRef

Medina, V. B., Valdes, R. C., Yanez-Suarez, O., Garza-Jinich, M., and Lerallut, J.-F., Automatic initialization for a snakes-based cardiac contour extraction. IEEE EMBS Proc., 22nd Annual Int. Conf., Chicago, U.S.A, 3:1625–1628, July, 2000.

Yoon, S. W., Lee, H. K., Kim, J. H., and Lee, M. H., Medical endoscopic image segmentation using snakes. IEICE Trans. Inf. & Sys E87-D:3785–789, 2004, Mar.

Chuang, C.-H., and Lie, W.-N., Automatic snake contours for the segmentation of multiple objects. The 2001 IEEE Int. Symposium on Circuits and Systems (ISCAS), Sydney, Australia, 2:389–392, 2001, May.

Xu, C., and Prince, J. L., Gradient vector flow: A new external force for snakes. IEEE Proc., Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Juan, pp. 66–71, 1997, June.

Cohen, L. D., and Cohen, I., Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. on Pattern Anal. Machine Intell 15:111131–1147, 1993, Nov.CrossRef

10.

Williams, D. J., and Shah, M., A fast algorithm for active contours and curvature estimation. CVGIP: Image Underst. 55:114–26, 1992.MATHCrossRef

11.

Nava F. P., and Martel, A. F., Wavelet modeling of contour deformation in Sobolev spaces for fitting and tracking applications. Pattern Recogn. 36:1119–1130, 2003.MATHCrossRef

12.

Burrus C. S., Gopinath, R. A., and Guo, H., Introduction to Wavelets and Wavelet Transforms. New Jersey, Prentice-Hall, Inc., 1998.

13.

McInerney, T., and Terzopoulos, D., Deformable models in medical image analysis. Proc. Workshop on Mathematical Methods in Biomedical Image Analysis. San Francisco, U.S.A., 171–180, June, 1996.

Titel: Wavelet-based Multi-resolution Deformation for Medical Endoscopic Image Segmentation
verfasst von: Sung Won Yoon
Chungkeun Lee
Jin Kwon Kim
Myoungho Lee
Publikationsdatum: 01.06.2008
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 3/2008
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-007-9124-6

Springer Medizin

Abstract

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