|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 50 - Number 3
|Year of Publication: 2012
|Authors: P.s. Hiremath, Prema T. Akkasaligar, Sharan Badiger
P.s. Hiremath, Prema T. Akkasaligar, Sharan Badiger . Linear Regression Model for Gaussian Noise Estimation and Removal for Medical Ultrasound Images. International Journal of Computer Applications. 50, 3 ( July 2012), 11-15. DOI=10.5120/7750-0808
Ultrasound imaging is widely used in the field of medicine. It is used for imaging soft tissues in organs like liver, kidney, spleen, uterus, heart, brain etc. The common problem in ultrasound image is speckle noise which is caused by the imaging technique used, that may be based on coherent waves such as acoustic to laser imaging. The denoising is to be performed to improve the image quality for more accurate diagnosis. The objective of the paper is to propose a novel linear regression model for Gaussian representation of speckle noise in medical ultrasound images. The speckle noise is modelled as a Gaussian noise, with estimated mean and standard deviation based on PSNR of the ultrasound image, using the proposed linear model for Gaussian noise estimation and removal. The experimental results demonstrate the efficacy of the proposed method.