Document Detail


Rayleigh-maximum-likelihood filtering for speckle reduction of ultrasound images.
MedLine Citation:
PMID:  17518065     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Speckle is a multiplicative noise that degrades ultrasound images. Recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need for more robust despeckling techniques, for both routine clinical practice and teleconsultation. Methods previously proposed for speckle reduction suffer from two major limitations: 1) noise attenuation is not sufficient, especially in the smooth and background areas; 2) existing methods do not sufficiently preserve or enhance edges--they only inhibit smoothing near edges. In this paper, we propose a novel technique that is capable of reducing the speckle more effectively than previous methods and jointly enhancing the edge information, rather than just inhibiting smoothing. The proposed method utilizes the Rayleigh distribution to model the speckle and adopts the robust maximum-likelihood estimation approach. The resulting estimator is statistically analyzed through first and second moment derivations. A tuning parameter that naturally evolves in the estimation equation is analyzed, and an adaptive method utilizing the instantaneous coefficient of variation is proposed to adjust this parameter. To further tailor performance, a weighted version of the proposed estimator is introduced to exploit varying statistics of input samples. Finally, the proposed method is evaluated and compared to well-accepted methods through simulations utilizing synthetic and real ultrasound data.
Authors:
Tuncer C Aysal; Kenneth E Barner
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  26     ISSN:  0278-0062     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  2007 May 
Date Detail:
Created Date:  2007-05-23     Completed Date:  2007-06-19     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  712-27     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA. aysal@mail.eecis.udel.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artifacts*
Computer Simulation
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Likelihood Functions
Models, Biological
Models, Statistical
Reproducibility of Results
Sensitivity and Specificity
Ultrasonography, Prenatal / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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