Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

Applied Optics
Pablo EtcheparebordaGuillermo H Kaufmann

Abstract

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

References

Jun 9, 2005·IEEE Transactions on Neural Networks·Rui Xu, Donald Wunsch
Dec 25, 2007·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Donald D Duncan, Sean J Kirkpatrick
Jan 1, 1991·IEEE Transactions on Neural Networks·B Kosko

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Citations

Feb 2, 2021·Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography·Alban GallardErwan Donal

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