Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

2005 ◽  
Vol 152 (1) ◽  
pp. 129 ◽  
Author(s):  
S. Gupta ◽  
R.C. Chauhan ◽  
S.C. Saxena
Keyword(s):  
Rayleigh Distribution ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Wavelet Domain ◽  
Adaptive Wavelet ◽  
Locally Adaptive

Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

2019 ◽  
Vol 28 (10) ◽  
pp. 1950176 ◽  
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Contrast Enhancement ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Gamma Correction ◽  
Significant Information ◽  
Adaptive Wavelet ◽  
Low Contrast ◽  
Image Contrast Enhancement

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors

2008 ◽  
Vol 55 (9) ◽  
pp. 2152-2160 ◽  
Author(s):  
Hossein Rabbani ◽  
Mansur Vafadust ◽  
Purang Abolmaesumi ◽  
Saeed Gazor
Keyword(s):  
Noise Reduction ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Wavelet Domain ◽  
Complex Wavelet ◽  
Speckle Noise Reduction

Wavelet domain Bayesian processor for speckle removal in medical ultrasound images

2012 ◽  
Vol 6 (5) ◽  
pp. 580 ◽  
Author(s):  
M. Amirmazlaghani ◽  
H. Amindavar
Keyword(s):  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Wavelet Domain

scholarly journals Motion blur invariant for estimating motion parameters of medical ultrasound images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Barmak Honarvar Shakibaei Asli ◽  
Yifan Zhao ◽  
John Ahmet Erkoyuncu
Keyword(s):  
Image Quality Assessment ◽  
Medical Image Analysis ◽  
Main Idea ◽  
Delta Function ◽  
Motion Blur ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Fetal Ultrasound ◽  
Dirac Delta ◽  
Motion Parameters

AbstractHigh-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

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