• DocumentCode
    3309982
  • Title

    A neural network approach for estimating large K distribution parameters

  • Author

    Smolíková, Renata ; Wachowiak, Mark P. ; Zurada, Jacek M. ; Elmaghraby, Adel S.

  • Author_Institution
    Comput. Sci. & Eng. Program, Louisville Univ., KY, USA
  • Volume
    3
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    2139
  • Abstract
    The K distribution has been proposed in the literature as a general speckle model for ultrasonic backscatter. The shape parameter of this distribution can be used to provide clinically important information on tissue density and regularity. A neural approach for parameter estimation is proposed, specifically for large values of the shape parameter. Experimental results on simulated images show that this approach compares favorably with other methods. Thus, neural networks can be used in conjunction with other approaches to accurately model speckle, and thereby to classify tissue
  • Keywords
    Gaussian noise; backscatter; biological tissues; biomedical ultrasonics; feedforward neural nets; medical image processing; parameter estimation; probability; speckle; general speckle model; large K distribution parameters; neural network approach; parameter estimation; tissue density; tissue regularity; ultrasonic backscatter; Biomedical imaging; Computer science; Distributed computing; Frequency; Neural networks; Parameter estimation; Rayleigh scattering; Shape; Speckle; Ultrasonic imaging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.938497
  • Filename
    938497