Threshold based lossy compression of medical ultrasound images using contourlet transform

Currently,the ultrasound image has been widely used in diagnosis and treatment of clinical medicine,the results obtained by the diagnostic accuracy and reliability of the image is directly related to the effects of diagnosis and treatment.Because ultrasound images in the imaging process inevitably contaminated noise,thus the research of inhibiting ultrasound image noise is one of the important issues in domestic and international ultrasound imaging techniques.This paper studies the multi-scale analysis and wavelet thresholding two theories,put forwarda denoising algorithm about combining the Nonsubsampling contourlet transform and a new threshold function,experiments show that the new algorithm can not only good at suppressing the noise of ultrasound images,and can better retain image edge and texture details.

Download Full-text

Performance Comparison of Wavelet Transform and Contourlet Transform based methods for Despeckling Medical Ultrasound Images

International Journal of Computer Applications ◽

10.5120/3130-4313 ◽

2011 ◽

Vol 26 (9) ◽

pp. 34-41 ◽

Cited By ~ 6

Author(s):

P.S. Hiremath ◽

Prema T. Akkasaligar ◽

Sharan Badiger

Keyword(s):

Wavelet Transform ◽

Performance Comparison ◽

Contourlet Transform ◽

Ultrasound Images ◽

Medical Ultrasound

Download Full-text

Modeling of Log-Transformed Speckle Noise in the Contourlet Transform Domain for Medical Ultrasound Images

Journal of Circuits System and Computers ◽

10.1142/s0218126615400046 ◽

2014 ◽

Vol 24 (02) ◽

pp. 1540004 ◽

Cited By ~ 1

Author(s):

Shahriar Mahmud Kabir ◽

Mohammed Imamul Hassan Bhuiyan

Keyword(s):

Estimation Method ◽

Speckle Noise ◽

Diagnostic Value ◽

Contourlet Transform ◽

Speckle Reduction ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Transform Domain ◽

Processing Step ◽

Bessel K Form

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important for developing effective homomorphic despeckling techniques, the most popular approach of speckle reduction from ultrasound images. In this paper, the bessel K-form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is introduced for estimating the parameters of the BKF pdf. The effectiveness of the proposed estimation method is demonstrated using Monte Carlo simulations. The appropriateness of the BKF pdf in modeling the speckle is first studied extensively for simulated noise of different levels in the contourlet transform domain. Next, the suitability of BKF model is investigated for the case of real US images that include neonatal brain and breast tumors. It is shown that, in general the BKF prior can model the statistics of the contourlet transform coefficients corresponding to the log-transformed speckle better than the traditionally used Gaussian, normal inverse Gaussian and generalized Nakagami pdfs.

Download Full-text

Despeckling of medical ultrasound images using data and rate adaptive lossy compression

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2005.847401 ◽

2005 ◽

Vol 24 (6) ◽

pp. 743-754 ◽

Cited By ~ 61

Author(s):

N. Gupta ◽

M.N.S. Swamy ◽

E. Plotkin

Keyword(s):

Lossy Compression ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Rate Adaptive ◽

Using Data

Download Full-text

Motion blur invariant for estimating motion parameters of medical ultrasound images

Scientific Reports ◽

10.1038/s41598-021-93636-4 ◽

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.

Download Full-text