access icon free Using a generalised method of moment approach and 2D-generalised autoregressive conditional heteroscedasticity modelling for denoising ultrasound images

This study presents a novel approach for ultrasound (US) images denoising. It concerns a class of generalised method of moments estimators with interesting asymptotic properties for wavelet coefficients 2D generalised autoregressive conditional heteroscedasticity modelling. Afterwards, these estimators can be used for removing noise from US images. Indeed, a minimum mean -square error method is applied for estimating the clean wavelet image coefficients. To judge the quality of the denoising procedure, a link between the denoising efficiency procedure and a proposed asymmetry measure is established. Several tests have been carried out to prove the performance of the proposed approach. The obtained results are compared with those of contemporary image denoising methods using usual image quality assessment metrics and two proposed no-reference quality metrics.

Inspec keywords: wavelet transforms; medical image processing; autoregressive processes; biomedical ultrasonics; method of moments; least mean squares methods; image denoising

Other keywords: generalised method-of-moments estimators; ultrasound image denoising; denoising efficiency procedure; usual image quality assessment metrics; no-reference quality metrics; asymmetry measure; clean wavelet image coefficients; minimum mean-square error method; 2D-generalised autoregressive conditional heteroscedasticity modelling; noise removal; generalised method-of-moment approach

Subjects: Patient diagnostic methods and instrumentation; Sonic and ultrasonic radiation (biomedical imaging/measurement); Other topics in statistics; Integral transforms in numerical analysis; Other topics in statistics; Optical, image and video signal processing; Integral transforms in numerical analysis; Interpolation and function approximation (numerical analysis); Numerical approximation and analysis; Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Sonic and ultrasonic radiation (medical uses); Probability theory, stochastic processes, and statistics; Biology and medical computing

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5528
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