Using a generalised method of moment approach and 2D‐generalised autoregressive conditional heteroscedasticity modelling for denoising ultrasound images. Issue 11 (24th September 2018)

• Record Type: Journal Article
• Authors: Raslain, Safia; Hachouf, Fella; Kharfouchi, Soumia
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 2011
• Page End: 2022
• Publication Date: 2018-09-24
• Subjects: biomedical ultrasonics -- medical image processing -- image denoising -- method of moments -- least mean squares methods -- autoregressive processes -- wavelet transforms; generalised method‐of‐moment approach -- 2D‐generalised autoregressive conditional heteroscedasticity modelling -- ultrasound image denoising -- generalised method‐of‐moments estimators -- noise removal -- minimum mean‐square error method -- clean wavelet image coefficients -- denoising efficiency procedure -- asymmetry measure -- usual image quality assessment metrics -- no‐reference quality metrics; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5528
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.