Rayleigh-Maximum-Likelihood Filtering for Speckle Reduction of Ultrasound Images

• Published in: IEEE transactions on medical imaging Vol. 26; no. 5; pp. 712 - 727
• Main Authors: Aysal, T.C., Barner, K.E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filtering; Algorithms; Attenuation; Background noise; Clinical medicine; Computer Simulation; Degradation; Estimators; Filtering; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Instruments; Likelihood Functions; Mathematical models; maximum likelihood estimation; Methods; min/max filtering; Models, Biological; Models, Statistical; multiplicative noise; Noise; Noise reduction; Noise robustness; Preserves; Reduction; Reproducibility of Results; Sensitivity and Specificity; Smoothing; Smoothing methods; Speckle; speckle reduction; Studies; Ultrasonic imaging; Ultrasonography, Prenatal - methods; Ultrasound; ultrasound imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2007.895484

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