Minimum Requirement of Artificial Noise Level for Using Noise-Assisted Correlation Algorithm to Suppress Artifacts in Ultrasonic Nakagami Images

The Nakagami image is a complementary imaging mode for pulse-echo ultrasound B-scan to characterize tissues. White noise in anechoic areas induces artifacts in the Nakagami image. Recently, we proposed a noise-assisted correlation algorithm (NCA) for suppressing the Nakagami artifact. In the NCA, artificial white noise is intentionally added twice to backscattered signals to produce two noisy data, which are used to establish a correlation profile for rejecting noise. This study explored the effects of artificial noise level on the NCA to suppress the artifact of the Nakagami image. Simulations were conducted to produce B-mode images of anechoic regions under signal-to-noise ratios (SNRs) of 20, 10 and 5 dB. Various artificial noise levels ranging from 0.1- to 1-fold of the intrinsic noise amplitude were used in the NCA for constructing the Nakagami images. Phantom experiments were conducted to validate the performance of using the optimal artificial noise level suggested by the simulation results to suppress the Nakagami artifacts by the NCA. The simulation results indicated that the artifacts of the Nakagami image in the anechoic regions can be gradually suppressed by increasing the artificial noise level used in the NCA to improve the image contrast-to-noise ratio (CNR). The CNR of the Nakagami image reached 20 dB when the artificial noise level was 0.7-fold of the intrinsic noise amplitude. This criterion was demonstrated by the phantom results to provide the NCA with an excellent ability to obtain artifact-free Nakagami images.

[1]  P. Shankar A general statistical model for ultrasonic backscattering from tissues , 2000 .

[2]  Purang Abolmaesumi,et al.  Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors , 2008, IEEE Transactions on Biomedical Engineering.

[3]  G. Cloutier,et al.  A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. , 2010, Ultrasound in medicine & biology.

[4]  Chien-Cheng Chang,et al.  Using ultrasound Nakagami imaging to assess liver fibrosis in rats. , 2012, Ultrasonics.

[5]  F Davignon,et al.  A parametric imaging approach for the segmentation of ultrasound data. , 2005, Ultrasonics.

[6]  C. R. Hill,et al.  Acoustic properties of normal and cancerous human liver-II. Dependence of tissue structure. , 1981, Ultrasound in medicine & biology.

[7]  Chih-Chung Huang,et al.  Effect of Adaptive Threshold Filtering on Ultrasonic Nakagami Parameter to Detect Variation in Scatterer Concentration , 2010, Ultrasonic imaging.

[8]  Chien-Cheng Chang,et al.  Microvascular Flow Estimation by Contrast-Assisted Ultrasound B-Scan and Statistical Parametric Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[9]  Chien-Cheng Chang,et al.  Imaging local scatterer concentrations by the Nakagami statistical model. , 2007, Ultrasound in medicine & biology.

[10]  P. Shankar Statistical modeling of scattering from biological media , 2002 .

[11]  Gozde Bozdagi Akar,et al.  An adaptive speckle suppression filter for medical ultrasonic imaging , 1995, IEEE Trans. Medical Imaging.

[12]  G Cloutier,et al.  A system-based approach to modeling the ultrasound signal backscattered by red blood cells. , 1999, Biophysical journal.

[13]  Chien-Cheng Chang,et al.  Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study , 2008, Physics in medicine and biology.

[14]  R S Anand,et al.  Speckle reduction in ultrasound medical images using adaptive filter based on second order statistics , 2007, Journal of medical engineering & technology.

[15]  P. Tsui,et al.  The effect of transducer characteristics on the estimation of Nakagami paramater as a function of scatterer concentration. , 2004, Ultrasound in medicine & biology.

[16]  Shyh-Hau Wang,et al.  Quantitative analysis of noise influence on the detection of scatterer concentration by Nakagami parameter , 2005 .

[17]  Chien-Cheng Chang,et al.  Performance Evaluation of Ultrasonic Nakagami Image in Tissue Characterization , 2008, Ultrasonic imaging.

[18]  C. R. Hill,et al.  Acoustic properties of normal and cancerous human liver-I. Dependence on pathological condition. , 1981, Ultrasound in medicine & biology.

[19]  S Akselrod,et al.  The distribution of the local entropy in ultrasound images. , 1996, Ultrasound in medicine & biology.

[20]  N. de Jong,et al.  A new ultrasound contrast imaging approach based on the combination of multiple imaging pulses and a separate release burst , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[21]  Chien-Cheng Chang,et al.  Feasibility study of using high-frequency ultrasonic Nakagami imaging for characterizing the cataract lens in vitro. , 2007, Physics in medicine and biology.

[22]  Yin-Yin Liao,et al.  Classification of Benign and Malignant Breast Tumors by 2-D Analysis Based on Contour Description and Scatterer Characterization , 2010, IEEE Transactions on Medical Imaging.

[23]  Po-Hsiang Tsui,et al.  Noise Effect on the Performance of Nakagami Image in Ultrasound Tissue Characterization , 2008 .

[24]  Georgia D. Tourassi,et al.  General ultrasound speckle models in determining scatterer density , 2002, SPIE Medical Imaging.

[25]  R. F. Wagner,et al.  Describing small-scale structure in random media using pulse-echo ultrasound. , 1990, The Journal of the Acoustical Society of America.

[26]  Qifa Zhou,et al.  Cataract measurement by estimating the ultrasonic statistical parameter using an ultrasound needle transducer: an in vitro study. , 2011, Physiological measurement.

[27]  Chih-Chung Huang,et al.  Characterization of lamina propria and vocal muscle in human vocal fold tissue by ultrasound Nakagami imaging. , 2011, Medical physics.