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Medical Image Segmentation Based on Wavelet Analysis and Gradient Vector Flow
Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
- 1 Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
- 2 Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
- 3 Department of Software Engineering, University of Science and Technology Liaoning, Anshan, China
Journal of Software Engineering and Applications·Volume 07 (2014)·Pages 1019–1030·Published 13 November 2014·DOI10.4236/jsea.2014.712089
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Abstract
Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.
KeywordsPattern RecognitionImage SegmentationGVF Snake ModelWavelet Multi-Scale AnalysisMedical Image
- Gao, J., Li, X.W. and Zhang, J. (2007) The Research of Image Preprocessing Algorithm in Color Print Quality Inspection System. Packaging Engineering, 28, 64-66.
- Feng, J.H. and Gong, J.L. (2010) Improved Gradient Vector Flow Model and Its Applications. Journal of Numerical Methods and Computer Applications, 31, 30-38.
- Li, K.C. and Lu, J. (2013) Progress in Medical Imaging. Journal of Capital Medical University, 34, 364-367.
- Pham, D.L., Xu, C.Y. and Prince, J.L. (1998) A Survey of Current Method in Medical Image Segmentation. Technical Report JHU/ECE 99-01, Johns Hopkins University.
- Kass, M., Witkin, A. and Terzopoulos, D. (1987) Snakes: Active Contour Models. International Journal of Computer Vision, 1, 321-331,.
- Cohen, L.D. and Cohen, I. (1993) Finite-Element Methods for Active Contour Models and Balloons for 2D and 3D Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 1131-1147.
- McInerney, T. and Terzopoulos, D. (1995) A Dynamic Finite Element Surface Model for Segmentation and Tracking in Multidimensional Medical Images with Application to Cardiac 4D Image Analysis. Computerized Medical Imaging and Graphics, 19, 69-83.
- Terzopoulos, D. and Fleischer, K. (1988) Deformable Models. The Visual Computer, 4, 306-331.
- Caselles, V., Catte, F., Coll, T. and Dibos, F. (1993) A Geometric Model for Active Contours. Numerische Mathematik, 66, 1-31.
- Chan, T. and Vese, L.A. (2001) Active Contours without Edges. IEEE Transactions on Image Processing, 10, 266-277.
- Li, C.M., Xu, C.Y., Gui, C.F. and Fox, M.D. (2005) Level Set Evolution without Re-Initialization: A New Variational Formulation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 430-436.
- Xu, C.Y., Yezzi, A. and Prince, J.L. (2000) On Relationship between Parametric and Geometric Active Contours. Proceedings of 34th Asilomar Conference on Signal, Systems and Computers, Pacific Grove, 29 October-1 November 2000, 483-489.
- Xu, C.Y. and Prince, J.L. (1998) Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing, 7, 359-369. http://dx.doi.org/10.1109/83.661186
- Xu, C.Y. and Prince, J.L. (2000) Gradient Vector Flow Deformable Models. In: Handbook of Medical Imaging.
- Alomari, R.S., Kompalli, S. and Chaudhary, V. (2008) Segmentation of the Liver from Abdominal CT Using Markov Random Field Model and GVF Snakes. Proceedings-CISIS 2008: 2nd International Conference on Complex, Intelligent and Software Intensive Systems, Barcelona, 4-7 March 2008, 293-298.