Artículos

Two Robust Techniques for Segmentation of Biomedical Images

Dos Técnicas Robustas para la Segmentación de Imágenes Biomédicas

Roberto Rodríguez, Patricio J. Castillo, Valia Guerra¹, Ana G. Suárez and Ebroul Izquierdo²

Digital Signal Processing Group
1 Numerical Methods Group
Institute of Cybernetics, Mathematics & Physics (ICIMAF)

2 Department of Electronic Engineering, Queen Mary, London University

E–mails: rrm@icmf.inf.cu, vguerra@icmf.inf.cu, anagloria@icmf.inf.cu

Article received on October 15, 2004; accepted on August 18, 2006

Abstract

Image segmentation plays an important role in many systems of computer vision. According to criterions of many authors the segmentation finishes when it satisfies the goals of the observer. For that reason, an only method there is not able of solving all the problems that exists in the present time. In this work, we carry out a comparison between two segmentation techniques; namely, through the mean shift, where we give a new algorithm, and by using spectral methods. In the paper we discuss, through examples with biomedical real images, the advantages and disadvantages of them.

Keywords: Image segmentation, mean shift, spectral methods.

Resumen

La segmentación de imagen juega un importante rol en muchos sistemas de visión por computadora. Acorde al criterio de muchos autores la segmentación finaliza cuando son satisfechos los objetivos del observador. Por esa razón, no hay un único método capaz de resolver todos los problemas que existen en la época actual. En este trabajo, nosotros llevamos a cabo una comparación entre dos técnicas de segmentación; a saber, a través de la media desplazada, donde nosotros ofrecemos un nuevo algortimo, y usando los métodos espectrales. En el artículo nosotros discutimos, a través de ejemplos con imágenes biomédicas reales, las ventajas y desventajas de ellos.

Palabras claves: Segmentación de imagen, media desplazada, métodos espectrales.

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References

1. W. Kenong, D. Gauthier and M. D. Levine: "Live Cell Image Segmentation", IEEE Transactions on Biomedical Engineering, vol.42, no. 1, Jun. 1995.        [ Links ]

2. J. Sijbers, P. Scheunders, M. Verhoye, A. Van der Linden, D. Van Dyck, E. Raman: "Watershed–based segmentation of 3D MR data for volume quatization", Magnetic Resonance Imaging, vol. 15, no. 6, pp 679–688, 1997.        [ Links ]

3. C. Chin–Hsing, J. Lee, J. Wang and C. W. Mao: "Color image segmentation for bladder cancer diagnosis", Mathl. Comput. Modeling, vol. 27, no. 2, pp. 103–120, 1998.        [ Links ]

4. Rodríguez, R., Alarcón, T., Wong, R. and Sanchez, L.: "Color segmentation applied to study of the angiogenesis. Part I", Journal of Intelligent and Robotic System, Vol. 34, No.1, May 2002.        [ Links ]

5. P. Schmid: "Segmentation of digitized dermatoscopic images by two–dimensional color clustering", IEEE Trans. Med. Imag., vol. 18, no. 2, Feb., 1999.        [ Links ]

6. J.E. Koss, F. D. Newman, T. K. Johnson and D. L. Kirch: "Abdominal organ segmentation using texture transforms and a hopfield neural network", IEEE Trans. Med. Imag., vol. 18, no. 7, July, 1999.        [ Links ]

7. Comaniciu, D. I.: "Nonparametric Robust Method for Computer Vision", Ph.D. thesis, New Brunswick, Rutgers, The State University of New Jersey, January, 2000.        [ Links ]

8. Comaniciu, D. and Meer, P.: "Mean Shift: A Robust Approach toward Feature Space Analysis", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, May 2002.        [ Links ]

9. Fukunaga, K. and Hostetler, L. D.: "The Estimation of the Gradient of a Density Function", IEEE Trans., Information Theory, Vol. 21, pp. 32–40, 1975.        [ Links ]

10. Cheng, Y.: "Mean Shift, Mode Seeking, and Clustering", IEEE Trans., Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, pp. 790–799, Aug. 1995.        [ Links ]

11. Castillo, Patricio J.: "Robust Technique for Blood Vessels Segmentation in Malignant Tumors.", (In Spanish), Diploma Thesis, Mathematic Faculty, Havana University, 2004.        [ Links ]

12. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, No. 8, pp. 888–905, 2000.        [ Links ]

13. C. Fowlkes, S. Belongie, F. Chung, and J. Malik, "Spectral Grouping Using the Nystrom method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, No. 2, pp. 214–225, 2004.        [ Links ]

14. Rodríguez, R., Alarcón, T and Sánchez, L.: "MADIP: Morphometrical Analysis by Digital Image Processing", Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, Vol. I, pp. 291–298, ISBN 84–8021–349–3, 2001, Spain.        [ Links ]

15. E. Izquierdo and V. Guerra, "Numerical Stability of Nystrom Extension for Image Segmentation", Proceeding of 2004 IEEE International Workshop on Machine Learning for Signal Processing, Sept, 2004, Sao Luis, Brasil.        [ Links ]