Classification and Estimation of Ultrasound Speckle Noise with Neural Networks

Authors:
M. P. Wachowiak;A. S. Elmaghraby;R. Smolikova;J. M. Zurada
Affiliations:
-;-;-;-
Venue:
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Year:
2000

Citing 0
Cited 3

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

MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Nonlocal means-based speckle filtering for ultrasound images

IEEE Transactions on Image Processing
Real time ultrasound image denoising

Journal of Real-Time Image Processing

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Abstract

Presents a neural-based approach to classifying and estimating the statistical parameters of speckle noise found in biomedical ultrasound images. Speckle noise, a very complex phenomenon, has been modeled in a variety of different ways: and there is currently no clear consensus as to its precise statistical characteristics. In this study, different neural network architectures are used to classify ultrasound images contaminated with three types of noise, based upon three one-parameter statistical distributions. At the same time: the parameter is estimated. It is expected that accurate characterization of ultrasound speckle noise will benefit existing post-processing methods, and may lead to new refinements in these techniques.