Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image

2008 ◽  
Vol 13 (5) ◽  
pp. 553-558 ◽  
Author(s):  
Xiao-yang Song ◽  
Ya-zhu Chen ◽  
Su Zhang ◽  
Wei Yang
2014 ◽  
Vol 644-650 ◽  
pp. 3999-4004
Author(s):  
Min Fen Shen ◽  
Fei Huang ◽  
Zhi Fei Su ◽  
Li Sha Sun

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.


2014 ◽  
Vol 24 (02) ◽  
pp. 1540004 ◽  
Author(s):  
Shahriar Mahmud Kabir ◽  
Mohammed Imamul Hassan Bhuiyan

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.


2021 ◽  
Author(s):  
Xinze Li ◽  
Wei Shi ◽  
Yang Jiao ◽  
Chen Yang ◽  
Ninghao Wang ◽  
...  

Author(s):  
Are Charles Jensen ◽  
Carl-Inge Colombo Nilsen ◽  
Andreas Austeng ◽  
Sven Peter Nasholm ◽  
Sverre Holm

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