Medical Ultrasonography Denoising Using Sparse Coding Shrinkage

Author

Xi Chen ; Nong Sang ; Haitao Gan ; Zhiping Dan ; Yanfei Chen ; Hexing Ren

Author_Institution

Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2013

fDate

26-28 July 2013

Firstpage

68

Lastpage

72

Abstract

A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of the new method. Results demonstrate that compared with classical denoising algorithms, the new method has increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. Our algorithm is also proved to be effective to the medical ultrasonography.

Keywords

Bayes methods; adaptive estimation; biomedical ultrasonics; image coding; image denoising; image segmentation; maximum likelihood estimation; medical image processing; Laplacian distribution; MAP estimate formula; PSNR; adaptive threshold; coding coefficient modelling; image sparse coding shrinkage; locally adaptive shrinkage Bayesian estimation; medical ultrasonography denoising; peak signal-to-noise ratio; subjective visual effect quality improvement; Biomedical imaging; Dictionaries; Image coding; Laplace equations; Noise; Noise reduction; Ultrasonic imaging; denoising; medical ultrasonography; sparse coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Graphics (ICIG), 2013 Seventh International Conference on

Conference_Location

Qingdao

Type

conf

DOI

10.1109/ICIG.2013.20

Filename

6643639