De-noising of CT Images using Combined Bivariate Shrinkage and Enhanced Total Variation Technique

Devanand Bhonsle*, Vivek Kumar Chandra**, G.R.Sinha***
* Senior Assistant Professor, Department of Electrical and Electronics Engineering, SSTC-SSGI, Faculty of Engineering & Technology, Bhilai, Chhattisgarh, India.
** Professor and Head, Department of Electrical and Electronics Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India.
*** Adjunct Professor, IIIT Banglore, Karnataka, India.
Periodicity:June - August'2018


The authors have developed a combined approach to reduce the effect of noise from the medical images. Noises are generally originated due to physical processes of imaging rather than in the tissue textures. Various types of noise signals, viz. photon, electronics, quantization, etc., often contribute to degrade the image quality. In general, the overall noise is assumed to be additive with a zero-mean, constant-variance Gaussian distribution, which is commonly known as Additive White Gaussian Noise (AWGN). In this paper, de-noising methods are applied on Computed Tomography (CT) images using a proposed combined method which combines bivariate thresholding and enhanced total variation methods using wavelet based image fusion technique. This method provides better results in terms of Peak Signal to Noise Ratio (PSNR).


Medical Images, CT Scan, De-noising, Additive White Gaussian Noise (AWGN), Dual Tree Complex Wavelet Transform (DTCWT), Total Variation.

How to Cite this Article?

Bhonsle. D., Chandra. V. K and Sinha. G. R. (2018). De-Noising of Medical Images Using Combined Bivariate Shrinkage and Enhanced Total Variation Technique. i-manager's Journal on Electronics Engineering, 8(4), 12-18.


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