Open AccessSpeckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain
Author(s) -
Gupta Deep,
Anand Radhey Shyam,
Tyagi Barjeev
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0330
Subject(s) - shearlet , speckle pattern , speckle noise , computer science , artificial intelligence , diffusion , domain (mathematical analysis) , linear filter , pattern recognition (psychology) , computer vision , image (mathematics) , mathematics , filter (signal processing) , physics , mathematical analysis , thermodynamics
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.