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. © Springer-Verlag Berlin Heidelberg 2001.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2123 LNAI, pp. 196–205). Springer Verlag. https://doi.org/10.1007/3-540-44596-x_16
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