Abstract
The Nakagami distribution is a model for the backscattered ultrasound echo from tissues. The Nakagami shape parameter m has been shown to be useful in tissue characterization. Many approaches to estimating this parameter have been reported. In this paper, a maximum likelihood estimator (MLE) is derived, and a solution method is proposed. It is also shown that a neural network can be trained to recognize parameters directly from data. Accuracy and consistency of these new estimators are compared to those of the inverse normalized variance, Tolparev-Polyakov, and Lorenz estimators.
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References
Abdi, A., Kaveh, M.: Performance Comparison of Three Different Estimators for the Nakagami m Parameter Using Monte Carlo Simulation. IEEE Communications Letters 4 (2000) 119–121
Alzer, H.: On Some Inequalities for the Gamma and Psi Functions. Math. Comp. 66 (1997) 373–389
Clifford, L., Fitzgerald, P., James, D.: Non-Rayleigh First-Order Statistics of Ultrasonic Backscatter from Normal Myocardum. Ultrasound in Med. and Biol. 19 (1993) 487–495
Iskander, D. R., Zoubir, A. M., Boashash, B.: A Method for Estimating the Parameters of the K Distribution. IEEE Trans. Sig. Proc. 47 (1991) 1147–1151
Liu, M. C., Kuo, W., Sastri, T.: An Exploratory Study of a Neural Network Approach for Reliability Data. Analysis Quality and Reliability Eng. Intl. 11 (1995) 107–112
Nakagami, M.: The m-distribution: A general formula of intensity distribution of rapid fading. in Statistical Methods in Radio Wave Propagation W. C. Hoffman, Ed. New York: Pergamon (1960) 3–36
Shankar, P. M.: A General Statistical Model for Ultrasonic Backscattering from Tissues. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control 47 (2000) 727–736
Smolýková, R., Wachowiak, M. P., Elmaghraby, A. S., Zurada, J. M.: A Neuro-Statistical Approach to Ultrasound Speckle Modeling. Proc. ISCA 13th Intl. Conf., Honolulu, HI (2000) 94–97
Wachowiak, M. P., Smolýková, R., Zurada, J. M., Elmaghraby, A. S.: A Neural Approach to Speckle Noise Modeling. Intelligent Engineering System Through Artificial Neural Networks: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining and Complex Systems 10 ASME Press, New York (2000) 837–842
Wachowiak, M. P., Smolýková, R., Elmaghraby, A. S., Zurada, J. M.: Classification and Estimation of Ultrasound Speckle Noise With Neural Networks. Proc. on IEEE International Symposium on Bio-Informatics and Biomedical Engineering. Arlington, Virginia (2000) 245–252
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Wachowiak, M.P., Smolíková, R., Milanova, M.G., Elmaghraby, A.S. (2001). Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_16
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DOI: https://doi.org/10.1007/3-540-44596-X_16
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