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Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution

  • Mark P. Wachowiak
  • Renata Smolíková
  • Mariofanna G. Milanova
  • Adel S. Elmaghraby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2123)

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.

Keywords

Maximum Likelihood Estimation Speckle Noise Neural Learning General Statistical Model Neural Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Mark P. Wachowiak
    • 1
  • Renata Smolíková
    • 1
    • 2
  • Mariofanna G. Milanova
    • 1
    • 3
  • Adel S. Elmaghraby
    • 1
  1. 1.Department of Computer Engineering and Computer ScienceUniversity of LouisvilleLouisvilleUSA
  2. 2.Institute for Research and Applications of Fuzzy ModelingUniversity of OstravaCzech Republic
  3. 3.ICSRBulgarian Academy of ScienceBulgaria

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