Analysis Techniques For Eliminating Noise In Medical Images Using Bivariate Shrinkage Method

Transcription

1 Analysis Techniques For Eliminating Noise In Medical Images Using Bivariate Shrinkage Method Ravi Mohan Rai Department of Electronic And Communication Shri Ram Institute of Technology Jabalpur, INDIA Urooz Jabeen M Tech (Digital Communication) Shri Ram Institute of Technology Jabalpur, INDIA Abstract-- Image denoising is a challenging process in digital image processing aiming at the removal of noise and is still a demanding problem for researchers. During acquisition and transmission, images are often corrupted and denoising is an essential step to improve the image quality. In medical imaging like CT-Scan, Magnetic Resonance Image (MRI), EEG, ECG accurate diagnosis is to be done. In Magnetic Resonance Imaging (MRI) images are typically corrupted with noise, which hinder the medical diagnosis based on these images. Noise can be introduced in an image by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Noise degrades the quality of images, suppressing structural details, thus create difficulties to medical diagnosis. Therefore, in medical image de-noising it is necessary to remove the noise while preserving important features. The medical imaging techniques play vital role in providing important information about the organ to the physician in a non invasive manner and help in detecting the disease as early as possible. Practically acquisition time in medical imaging is limited due to patient comfort and system requirement. Therefore, fast imaging is needed. But when the time resolution is improved, the noise may degrades the quality of images, blurring boundaries and suppressing structural details, thus bring difficulties to medical diagnosis. There are various techniques for noise removal from images like Wiener filter, Median filter, Average filter and Wavelet thresholding. Each technique has its assumptions, merits, and demerits. Our proposed method separates unknown signal sources without any prior knowledge. In this paper, we used wavelet based bivariate shrinkage method algorithm as denoising technique and compare its results with existing Wavelet Denoising. Performance results are evaluated in terms of metrics called Peak Signal-to-Noise Ratio (PSNR). Since noise in MR images is non gaussian, results show that proposed technique is a very appropriate analysis technique for eliminating noise in Medical images specially MRI. I. Introduction Transmission of visual information in form of images is common & major method, but during the transmission images are harmed by a noise Various types of noise are Gaussian noise salt & pepper noise, speckle noise, shot noise, white noise In gaussion noise, noise is additive in nature it is independent at each pixel & independent in signal strength in salt & pepper noise image is having dark pixel in bright region and bright pixel in dark region. It is caused by dead pixels, bit errors in transmission in shot noise it is type of electronic noise and it is caused when energy carrying particles are in optical device. Speckle noise is multiplicative in nature and it is exist in radars it is caused by coherent processing of back scattered signals.[2] So after receiving the image it needs denoising before using for any application. denoising procedure is removed noise from image and keep original image safe, but in process of denoising some blurring & artifacts are introduced in image.[1] Denoising procedure is compulsory in image processing because noise produces errors in reorganization denoising is used to removal of noise while retain signal features as much as All Rights Reserved 2013 IJARCET 2737

2 possible noise is also reduce the visual effects of image so enhancement is necessary. There are various types of denoising techniques are available.choice of technique are totally depend upon type of noise so it is necessary to have knowledge about noise for choosing proper algorithm. Images are effected by noise via capturing, transmission media and discrete source of radiation development of image denoising algorithm is based on the characteristics of image.on this basis there are lots of image denoising algorithms are developed like filtering techniques, wavelet based methods, independent component analysis etc All methods of denoising are belongs to low pass filter there are two types of filtering methods first in spatial domain and other in transform domain.filtering via transform domain introduces less artifacts and it is more efficient. Wavelet transform is a orthogonal transform in which high & low freq frequency of image can be well separated and results of this method is satisfying.[3] II. Proposed Algorithm To start the development of the program according to the proposed method we first develop the detailed flow of the program given below Equations and Matlab codes (functions) used for each block of flowchart is given below: 1 To clear all variables and command window contents and to close all previously opened windows following MATLAB functions are used clc; clear all; close all; 2 For reading image from any location in computer Imread function is used. 3 To avoid non squared matrix coefficient evaluation in wavelet image is resize in 512X512 matrix using Imresize MATLAB 4 Speckle noise is added using Imnoise MATLAB 5 Logarithm of noisy image is performed using log function to convert multiplicative noise model to additive noise model. 6 2 dimensional discrete wavelet transform of image is performed using dwt2 MATLAB 7 Many nan (not a number) and infinite values present in wavelet coefficient matrix because of logarithm of image. All these values are shifted to zero. For calculating infinite elements and nan elements isinf and isnan MATLAB functions are used 8 To estimate the noise variance σn2 from the noisy wavelet coefficients, a robust median estimator is used from the finest scale wavelet coefficients (HH1 sub band). σn2 = Median / ( yi ) yi is element of sub band HH1 (4.1) 9 Now signal variance is calculated. 10 Using bivariate shrinkage function and Bayesian estimation theory each coefficient is estimated. The expression for estimated coefficient is given by: n ( y1 y2 3 ). y1. w1 2 2 y1 y2 (4.2) For dead zone region where estimated wavelet coefficient is zero soft threshold technique is used to estimate wavelet coefficient. The soft shrinkage function can be written as: 2 2 n w( y) soft( y, (4.3) 11 Now max surrounding matrix is calculated. For calculating max surrounding matrix wavelet coefficient is divided into 9 elements and taking absolute value of the wavelet coefficient of any location in the neighboring region. ) 2738

3 12 Then estimated wavelet coefficient is modified according max surrounding matrix using following expression: wi = { wi, if wi = max(ω (i)), (4.4) w i, if w i # max(ω (i)), Where Ω (i) represent max surrounding matrix 13 Now inverse wavelet transform is performed to modified wavelet coefficient using idwt2 function of MATLAB. Exponential of previous data perform inverse operation of logarithm. 14 Convert the output to UINT8.This operation convert output to unsigned integer. 15 In any image 0 to 255 gray scale values exist. Floating point and negative values are not present. 16 Show the original image, noisy image and de noised image by all methods using Imshow 17 Calculate all Assessment parameters (PSNR and SSIM) for all methods to be compared. For edge comparison shows edges of denoised images of all methods. Then plot edge pixel per column and rows. Resulted image shown by linear filter, median filter III. Result In results we processed MRI image, results are shown. Resulted image of adaptive filter lee filter bivariate method & modified bivariate method 2739

4 Comparison chart are as follows- Comparison Chart. STATISTICAL MEASUREMENT PSNR SSIM Linear Filter Lee Filter Median Filter Adaptive Filter Bivariate Shrinkage Filter Modified Bivariate Shrinkage Filter STATISTICAL MEASUREMENT σn = PSNR SSIM Linear Filter Lee Filter Median Filter Adaptive Filter Bivariate Shrinkage Filter Modified Bivariate Shrinkage Filter σn = IV. Conclusions and Further Research In this we observe how wavelet transforms can be implemented to scale and translate a noise speckle image into a multi-resolution analysis representation. Bivariate shrinkage function used to reduce noise speckle at different resolution levels. We modify this function to give more appropriate results. These results obtained have shown significant speckle reduction and preserve line and edge area then standard filter methods such as Lee filter, Wiener, Linear and Median filter. In modified bivariate and bivariate method computation time is less compare to filter methods. SSIM and PSNR are two important parameters for measuring quality of image. Modified bivariate function gives best results for both parameters. Modified bivariate function preserve edge information as compare to filter methods and bivariate method. In filter methods images are over smoothed and blurred and their edge information is lost. REFRENCES [1] K. Karthikeyan,Dr. C. Chandrasekar, Speckle Noise Reduction of Medical Ultrasound Images using Bayesshrink Wavelet Threshold International Journal of Computer Applications ( ),Volume 22 No.9, May [2] Bhausaheb Shinde, Dnyandeo Mhaske, Machindra Patare, A.R. Dani, apply different TECHNIQUES TO REMOVE THE SPECKLE NOISE USING MEDICAL IMAGES, International Journal of Engineering Research and Applications (IJERA) ISSN: Vol. 2, Issue 1,Jan-Feb 2012, pp , [3] Kother Mohideen1, Arumuga Perumal2, Krishnan3 and Mohamed Sathik4 1Sadakathulla Image Denoising And Enhancement Using Multi wavelet With Hard Threshold In Digital Mammographic Images IJVC, vol. 79, no. 1, pp , [4] Microarray Image Enhancement by Denoising Using Stationary Wavelet Transform X. H. Wang, Robert S. H. Istepanian,, and Yong Hua Song, IEEE TRANSACTIONS ON NANOBIOSCIENCE, VOL. 2, NO. 4, DECEMBER 2008 [5] Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm Hui Ji and Cornelia Fermu ller, Member, IEEE, IEEE 2740

5 TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 4, APRIL [6] Hoˇsˇt alkov a, A.Proch azka, WAVELET SIGNAL AND IMAGE DENOISING E. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, no 3,may 2007 [7] T. Nabil SAR Image Filtering in Wavelet Domain by Sub band Depended Shrink Int. J. Open Problems Comp. Math., Vol. 2, No. 1, March 2009 [8] Duan Xinyu, Gao Guowei A Novel Waveletbased Denoising Method of SAR Image Using Interscale Dependency International Conference on Computer Science and Information Technology [9] Amandeep Kaur, Karamjeet Singh SPECKLE NOISE REDUCTION BY USING WAVELETS NCCI National Conference on Computational Instrumentation CSIO Chandigarh, INDIA, March 2010 [15] P. Lai, F. Huang, A. Larson, and D. Li, Fast 4d coronary MR angiography with k-t grappa, J. Magn. Reson. Imaging, vol. 27, pp ,2008. [16] C. Mistretta, O.Wieben, J. Velikina, W. Block, J. Perry, Y.Wu, and K. Johnson, Highly constrained backprojection for time-resolved MRI, Magn. Reson. Med., vol. 55, pp , [17] D. K. Hammonf and E. P. Simonceli, A machine learning framework for adaptive combination of signal denoising methods, presented at the Int. Conf. Image Process., [18] C. Kervrann and J. Boulanger, Local adaptivity to variable smoothness for exemplarbased image regularization and representation, IJVC, vol. 79, no. 1, pp , [10] Sendur and I. W. Selesnick, A bivariate shrinkage function for wavelet-based denoising, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Orlando, May 13-17, [11] Sendur and I. W. Selesnick, Bivariate shrinkage functions for wavelet-based denoising exploiting inter scale dependency, IEEE Trans. Signal Processing, vol. 50, pp , Nov [12] M. Griswold, P. Jakob, R. Heidemann, N. Mathias, V. Jellus, J.Wang, B. Kiefer, and A. Haase, Generalized autocalibrating partially parallel acquisitions (grappa), Magn. Reson. Med., vol. 47, pp , [13] F. Lin, T. Huang, N. Chen, F. Wang, and S. M. Stufflebeam, Functional mri using regularized parallel imaging acquisition, Magn. Reson. Med., vol. 54, pp , 2005 [14] T. Niendorf, C. Hardy, R. Giaquinto, P. Gross, H. Cline, Y. Zhu, G. Kenwood, S. Cohen, A. Grant, S. Joshi, N. Rofsky, and D. Sodickson, Toward single breath-hold whole-heart coverage coronary mra using highly accelerated parallel imaging with a 32- channel MR system, Magn. Reson. Med., vol. 56, pp ,