Open AccessSegmentation‐based MAP despeckling of medical ultrasound images in shearlet domain based on normal inverse Gaussian distribution
Author(s) -
Garg Amit,
Khandelwal Vineet
Publication year - 2020
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.2018.6347
Subject(s) - shearlet , speckle noise , smoothing , artificial intelligence , speckle pattern , maximum a posteriori estimation , pattern recognition (psychology) , noise reduction , computer science , mathematics , gaussian , noise (video) , computer vision , image (mathematics) , statistics , maximum likelihood , physics , quantum mechanics
Speckle noise is an undesirable phenomenon that exhibits granular pattern which reduces the diagnostic capability of clinical ultrasound (US) images. In this study, a new speckle denoising method based on modelling of detail band shearlet coefficients of log‐transformed US images is presented. In each detail shearlet subband, coefficients corresponding to signal and speckle noise are modelled as normal inverse Gaussian and Gaussian priors, respectively. These coefficients, based on image local statistics are segmented into different regions of heterogeneity viz. homogeneous, heterogeneous and strongly heterogeneous regions, respectively, so as to control over smoothing of denoised images. Then, using the prior distributions maximum a posteriori (MAP) estimation is performed over all regions of detail bands except those regions that represent strongly heterogeneous coefficients. For better performance, an adaptive weight function is also used in the MAP expression which reduces the loss of feature information. Experimentation is done on noise‐free synthetic kidney and foetus US images and a set of 60 real US images. The results are presented for objective and subjective quality assessment of the proposed method and five other methods for speckle denoising. The potential of the proposed method in comparison to other methods can easily be ascertained from the obtained denoising results.