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Optimally Discriminant Moments for Speckle Detection in Real B-Scan Images

  • Robert Martí
  • Joan Martí
  • Jordi Freixenet
  • Joan Carles Vilanova
  • Joaquim Barceló
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4478)

Abstract

Detection of speckle in ultrasound (US) images has been regarded as an important research topic in US imaging, mainly focusing on two specific applications: improving signal to noise ratio by removing speckle noise and, secondly, for detecting speckle patches in order to perform a 3D reconstruction based on speckle decorrelation measures.

A novel speckle detection proposal is presented here showing that detection can be improved based on finding optimally discriminant low order speckle statistics. We describe a fully automatic method for speckle detection and propose and validate a framework to be efficiently applied to real B-scan data, not being published to date. Quantitative and qualitative results are provided, both for real and simulated data.

Keywords

Class Separability Echo Amplitude Signal Speckle Suppression Classic Pattern Recognition Speckle Statistic 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Robert Martí
    • 1
  • Joan Martí
    • 1
  • Jordi Freixenet
    • 1
  • Joan Carles Vilanova
    • 2
  • Joaquim Barceló
    • 2
  1. 1.Computer Vision and Robotics Group. University of GironaSpain
  2. 2.Girona Magnetic Resonance Center. GironaSpain

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