Speckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain. Issue 2 (1st February 2015)

• Record Type: Journal Article
• Authors: Gupta, Deep; Anand, Radhey Shyam; Tyagi, Barjeev
• Is Part Of: IET image processing. Volume 9:Issue 2(2015)
• Page Start: 107
• Page End: 117
• Publication Date: 2015-02-01
• Subjects: biomedical ultrasonics -- medical image processing -- image texture -- image representation -- feature extraction -- edge detection -- transforms -- image denoising -- interference suppression -- image resolution -- filtering theory; speckle filtering -- ultrasound medical images -- modified nonlinear diffusion model -- nonsubsampled shearlet transform -- acquisition systems -- texture information -- despeckling method -- noisy coarser NSST coefficients -- localisation feature -- directionality feature -- multiscale feature -- image coefficient representation method -- nonlocal pixel information -- anisotropic diffusion process -- denoising efficiency improvement -- edge feature preservation -- neighbouring pixels -- adaptive grey variance -- noise suppression; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0330
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Speckle filtering is of great interest for the ultrasound medical images in which various noises and artefacts are introduced because of various limitations of the acquisition systems and techniques. Speckle is a prime factor to degrade the quality and most importantly, texture information present in the ultrasound images. This study presents a despeckling method based on a modified non‐linear diffusion model and non‐subsampled shearlet transform (NSST). As a new image representation method with the different features of localisation, directionality and multiscale, the NSST is utilised to provide the effective representation of the image coefficients. The modified anisotropic diffusion is applied to the noisy coarser NSST coefficients to improve the denoising efficiency and preserve the edge features effectively. In the diffusion process, the non‐local pixel information is incorporated to evaluate the gradient of eight connected neighbouring pixels with an adaptive grey variance. The performance of the proposed method is evaluated for both the standard test and real ultrasound images. Experimental results show that the proposed method produces better results of noise suppression with the preservation of more edges compared with several existing methods.