Abstract
We propose in this paper a robust adaptive region segmentation algorithm of dirty images in a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis. The algorithm can process both 2D and 3D data. In this work we focus on the adaptive character of the algorithm and we discuss how global and local statistics can be taken into account in the segmentation process. Results of segmentation performed on echocardiographic sequences (2D+T) and an evaluation of the performance of the proposed algorithm are presented.
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Keywords
- Discrete Wavelet Transform
- Segmentation Result
- Global Statistic
- Segmentation Process
- Echocardiographic Data
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References
Boukerroui, D., Basset, O., Guérin, N., Baskurt, A.: Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. European Journal of Ultrasound 8(2), 135–144 (1998)
Pappas, T.N.: An adaptive clustering algorithm for image segmentation. IEEE Transactions on Signal Processing SP-40(4), 901–914 (1992)
Muzzolini, R., Yang, Y.-H., Pierson, R.: Multiresolution texture segmentation with application to diagnostic ultrasound images. IEEE Transactions on Medical Imaging 12(1), 108–123 (1993)
Kervrann, C., Heitz, F.: Segmentation non supervisée des images naturelles texturées: approche statistique. Traitement du signal 11(1), 31–41 (1994)
Geman, D.: Random fields and inverse problems in imaging. In: Y. Vardi, M. (ed.) CAV 1998. LNCS, vol. 1427, pp. 161–172. Springer, Heidelberg (1998)
Vemuri, B.C., Rahman, S., Li, J.: Multiresolution adaptive K-means algorithm for segmentation of brain MRI. In: Intl. Compu. Sci. Conf. On Image Analysis and Comp. Graphics, Hong Kong, pp. 347–354 (1995)
Heitz, F., Perez, P., Bouthemy, P.: Multiscale minimization of global energy functions in some visual recovery problems. CVGIP: Image Understanding 59(1), 125–134 (1994)
Besag, B.J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society B 48(3), 259–302 (1986)
Besag, B.J.: Spatial interaction qnd the statistical analysis of lattice systems. Journal of the Royal Statistical Society B 26(2), 192–236 (1974)
Ashton, E.A., Parker, K.J.: Multiple resolution Bayesian segmentation of ultrasound images. Ultrasonic Imaging 17(2), 291–304 (1995)
Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Chalana, V., Kim, Y.: A methodology for evaluation of boundary detection algorithm on medical images. IEEE Transactions on Medical Imaging 16(1), 642–652 (1997)
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© 1999 Springer-Verlag Berlin Heidelberg
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Boukerroui, D., Basset, O., Baskurt, A., Noble, A. (1999). Segmentation of Echocardiographic Data. Multiresolution 2D and 3D Algorithm Based on Grey Level Statistics. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_56
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DOI: https://doi.org/10.1007/10704282_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66503-8
Online ISBN: 978-3-540-48232-1
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