INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

Transcription

1 INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK THE OPTIMAL APPROACH TO REDUCE POISSON NOISE IN X- RAY IMGES MR. SACHIN A. SAWALKAR 1, DR. R. M. DESHMUKH 2 1. M.E (Scholar) Electronic& telecommunication Engg, I.B.S.S.C.O.E, Amravati. 2. Head Of Department, Electronic &telecommunication Engg, I.B.S.S.C.O.E, Amravati. Accepted Date: 05/03/2015; Published Date: 01/05/2015 Abstract: An image is often corrupted by noise in its acquisition and transmission by various kinds of noises. Medical images have always been an important factor in diagnosis of disease. Poisson Noise in those images has always been a problem with the image clarity. Many important problems in engineering and science are well-modeled by Poisson noise, and the noise of medical X-ray images is Poisson noise. In this paper, we propose a method for noise removal for degraded medical X-ray images using improved preprocessing and an improved by Transform or spatial domain algorithm. Over the past years, various representation systems which sparsely approximate functions governed by anisotropic features such as edges in images have been proposed. However, one of the most common shortcomings of these frameworks is the lack of providing a unified treatment of the continuum and digital world, i.e., allowing a digital theory to be a natural digitization of the continuum theory. Shearlets were introduced as \ means to sparsely encode anisotropic singularities of multivariate data while providing a unified treatment of the continuous and digital realm. Keywords: x- ray Images, Position Noise Corresponding Author: MR. SACHIN A. SAWALKAR Access Online On: How to Cite This Article: PAPER-QR CODE 1123

2 INTRODUCTION X-radiation (composed of X-rays) is a form of electromagnetic radiation. Most X-rays have a wavelength in the range of 0.01 to 10 nanometers, corresponding to frequencies in the range 30 petahertz to 30 exahertz ( Hz to Hz) and energies in the range 100 ev to 100 kev. X-ray wavelengths are shorter than those of UV rays and typically longer than those of gamma rays. In many languages, X-radiation is referred to with terms meaning Rontgen radiation, after Wilhelm Rontgen, who is usually credited as its discoverer, and who had named it X-radiation to signify an unknown type of radiation. X-rays with photon energies above 5 10 kev (below nm wavelength) are called hard X- rays, while those with lower energy are called soft X-rays. Due to their penetrating ability, hard X-rays are widely used to image the inside of objects, e.g., in medical radiography and airport security. As a result, the term X-ray is metonymically used to refer to a radiographic image produced using this method, in addition to the method itself. When imaging with X-rays, an X-ray beam produced by a so-called X-ray tube passes through the body. On its way through the body, parts of the energy of the X-ray beam are absorbed. This process is described as attenuation of the X-ray beam. On the opposite side of the body, detectors or a film capture the attenuated X-rays, resulting in a clinical image. In conventional radiography, one 2D image is produced. In Computed Tomography, the tube and the detector are both rotating around the body during the examination so that multiple images can be acquired, resulting in a 3D visualization. Studies have shown that X-ray imaging system image degradation is mainly due to the system of random noise. X-ray generation and interaction with matter, in both time and space to satisfy Poisson random process. For fast X-ray imaging systems, due to the exposure time is short, X-ray quantum noise generated more prominent, seriously affecting the quality of the image. Today, we can deal with medical images by digital signal processing. Quality improvement of the degraded medical image is important, which is a current research topic. The medical data represented by photon images has a tendency to be degraded by Poisson noise. The characteristics of Poisson noise are determined by photon counting statistics. Because of this, it is a difficult task to remove the noise. However, if we can remove Poisson noise effectively, we will acquire a fine image from the degraded image produced with a small amount of photons. This may lead to a reduction of the probability of not only medical exposure, but also medical errors. 1124

3 Many author tried to denoise the X-ray medical images either by improving shrinkage techniques or moving to further improved transforms. N. Umadevi et al [5] proposed a hybrid model for Poisson noise reduction for X-ray images, they combine PURE shrink technique with biorthogonal Haar wavelet framework and got improvement in results compare to PURE shrink with Haar wavelet. Lingyan Du et al [2] made use of dual tree complex wavelet transform with Bayesian estimation for Poisson noise removal from X-ray images, as dual tree complex wavelet transform has property of approximate shift invariance and more directionality better results obtained compare to traditional discrete wavelet transform. V Vijay Kumar Raju, M Prema Kumar [1] uses dual tree complex wavelet transform and curvelet transform followed by soft and or hard threshold. MRI and X-ray images are used for experimentation and result comparison is done between dual tree complex wavelet transform and curvelet transform. In proposed work, we worked at intermediate level of transform and shrinkage. In general, after taking transform of noisy image direct thresholding of the coefficient is done. In our work we are limiting the number of coefficient to be thresholed and achieved improvement in result. We are using a concept of Harris corner detector which is normally used in watermarking to find out intensity variation of pixel coefficients called corner points with some modification and named it as Modified Harris operator. Poisson noise changes intensity of X-ray images and as Harris operator algorithm includes gradient finding operation which deals with intensity variation, we can use Harris operator in image denoisingdaubechies wavelet transform is used for decomposition of noisy image and threshold values are calculated using Bayes shrink. To every decomposed band of wavelet transform, we normalize the threshold value by multiplying it with maximum coefficient value of decomposed band. 1125

4 One can either normalize the sub-bands coefficients before thresholding or can upscale the threshold value to respective sub-band coefficient range. Then using soft thresholding, the respective decomposed bands are threshold. After thresholding, modified sub-band coefficient values are used to reconstruct the image. Finally PSNR is computed to check the workability of algorithm. Figure 1 shows the block diagram of our proposed work. MATLAB is used for algorithm development and testing purpose. The rest of paper is organized as follows. In section II, we explained some basic about Poisson noise and its behavior. In section III, we explain Modified Harris Operator. In section IV, we describe our proposed method of denoising. In section V we have shown the results and comparison and Section VI will conclude the paper II. POISSON NOISE AND ITS BEHAVIOR Noise is random by nature. An unwanted signal in information signal called as noise. In case of image, random modification in grey level values can be called as noise. Being random in nature, every noise follows some specific distribution. Poisson noise follows Poisson distribution as following Equation 1 and its graph shown in Figure 2. λ Probability of event occurrence per interval x No. of interval in given interval 1126

5 III. Thresholding Hard and soft thresholding techniques are used for purpose of image denoising. Keep and kill rule which is not only instinctively appealing but also introduces artifacts in the recovered images is the basis of hard thresholding whereas shrink and kill rule which shrinks the coefficients above the threshold in absolute value is the basis of soft thresholding. As soft thresholding gives more visually pleasant image and reduces the abrupt sharp changes that occurs in hard thresholding, therefore soft thresholding is preferred over hard thresholding The use of the soft thresholding method for the estimate of wavelet coefficients allows us to partially minimize the pseudo-gibbs phenomena. Moreover, the continuity of the softthreshold scheme can efficiently preserve the structure of the wavelet coefficients. However, the lowered wavelet coefficients lead to blurred boundary. Hao proposed a novel estimation approach of wavelet coefficient which is semi-soft thresholding. In this algorithm, adaptive preprocessing roughly removes ringing artifact, and improves blurred boundary. According to the links between wavelet coefficients and threshold value, the function The threshold is compared to all coefficients of the wavelet domain and when the coefficients are less than the threshold value they are assigned zero values, otherwise they are kept unaltered. The reason behind it is that small coefficients are supposed to be not of signal elements and so can be modified to zeroes. The large coefficients are supposed to be of important signal features. Some work in this area are performed by Weaver et al, Donoho and Johnstone, Jansen and Antoniadis. They proposed extensive wavelet thresholding techniques for denoising. In hard thresholding, the coefficients w=wxy which are less than a threshold λ are assigned to zeros. Otherwise they are kept unaltered. The hard thresholding technique is given in eqn. 13. One sample original image and corresponding hard and soft thresholding methods are presented in fig.2, where the threshold (λ) is assumed 0.4. Hard (w, λ) = w: w λ 0: w λ (13) In soft thresholding, the coefficients which are higher than the threshold are reduced by an amount equal to the value of threshold. Otherwise they are set to zeros. The soft thresholding technique is given in eqn.14 Soft (w, λ) = sgn w w λ +: w λ 0: w < λ (14) Where sgn (w) returns the sign of the w and a+ is defined in eqn

6 a + = a: a > 0 0: a 0 (15) Hard thresholding suffers from abrupt discontinuity which causes artefacts in the restored image. Soft thresholding causes the restored image over smoothing. There exist many more thresholding schemes those are compromise between the hard and soft thresholding schemes. IV. MODIFIED HARRIS OPERATOR Harris corner detector also called as Harris operator or Harris gradient detector. It has extensive use in Watermarking. It takes advantage of different regions of image. Image consists of mainly three regions namely flat region, edges and corner points as shown in Fig 3. For flat region, there is no change in intensity for all direction. For edges, intensity does not change in direction of edge. In case of corner points, we find intensity variation in all directions. A corner is a point for which there are two dominant and different edge directions in the vicinity of the point. In simpler terms, a corner can be defined as the intersection of two edges, where an edge is a sharp change in image brightness. 1128

7 In case of watermarking either 3x3 or 5x5 or any odd integer size window is applied to image to find corner points. Harris corner detector uses local autocorrelation function to find intensity variation in different directions and detects those points for which variation is high, it is given as follows M(i,j) is windowing function, X(i+a,j+b) is shifted intensity and 1129

8 X(i,j) is original intensity. EV(a,b) is intensity variation in different directions. The algorithm for Harris corner detector is explained in [3]. In case of denoising, we are including modification by decreasing window size to 2x2 and implying it on image in overlapping way as shown in Figure 4. Hence we are getting maximum number of corner points i.e. pixels with intensity variation. Algorithm of our proposed work is as follows, 1. Take noisy image as input 2. Apply Wavelet transform to image. 3. Find out threshold using Bayes Shrink method [9]. 4. Find out the corner points using modified Harris operator. 5. Apply soft threshold to that corner points only. 6. Upscale the threshold values for respective Sub-bands. 7. Apply threshold to diagonal, vertical and horizontal coefficients only, keep approximate coefficient untouched. 8. Apply inverse wavelet transform to reconstruct the denoised image back. 9. Calculate PSNR using following formulae Where, Imax is maximum grey value of image. MSE is mean square error and calculated as Where, I is original image, X is Denoised image. M and N are dimensions of original image 1130

9 V. RESULTS AND COMPARISON Proposed method is implemented and compared with different papers with same images used in them. Following are the comparison shown with different methods. Tables show the total number of coefficients processed by our method. Comparison is done on the basis of PSNR values. Comparison figure includes the denoised image and PSNR mention in available papers and calculated using Proposed Method. 1131

10 1132

11 1133

12 VI. CONCLUSION With the use modified Harris operator which include gradient operation, we are achieving approximate scale and rotation invariant property. Hence our proposed method producing nearly same and or improved results over dual tree complex wavelet transform and curvelet transform. Also in comparison with wavelet and direct thresholding, improved results are achieved. It is clear from table number 1 to 6 that all these improved results in PSNR are obtained without denoising whole sub-bands after decomposition. As per tables up to 95 percent of total coefficients are denoised. With this modified Harris operator it is possible for us to preserve edges of X-ray images compare to general algorithm using wavelet transform and thresholding and it is necessary in case of X-ray medical images. Here we use modified Harris operator with wavelet transform, hence future scope will be to use same operator or some operators which includes more directional properties with transforms giving higher directional coefficients like Shearlet transform. VII. REFERENCES 1. V Vijay Kumar Raju, M Prema Kumar, Denoising of MRI and X-Ray images using Dual Tree Complex Wavelet and Curvelet Transforms, International Conference on Communication and Signal Processing, April 3-5, 2014, India, IEEE 2. Lingyan Du,Yuqiao Wen, and Jiang Ma, Dual tree complex wavelet transform and Bayesian estimation based denoising of Poission-corrupted X-ray Images, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP) June 9 11, 2013, Beijing, China, / IEEE 1134

13 3. Xiao-Chen Yuan, Chi-Man Pun, Invariant Digital Image Watermarking Using Adaptive Harris Corner Detector, 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, / IEEE, DOI /CGIV Mohammad Reza Zare, Ahmed Mueen,Woo Chaw Seng, Mohammad Hamza Awedh, Combined Feature Extraction on Medical X-ray Images, Third International Conference on Computational Intelligence, Communication Systems and Networks, / IEEE DOI /CICSyN N. Umadevi et al Improved Hybrid Model For Denoising Poisson Corrupted Xray Images, International Journal on Computer Science and Engineering, Ling Wang, Jianming Lu, Yeqiu Li, Takashi Yahagi, And Takahide Okamoto, Noise Removal for Medical X-Ray Images in Wavelet Domain, Electrical Engineering in Japan, Vol. 163, No. 3, 2008, Wiley Periodicals, Inc. 7. Ling Wang, Jianming Lu, Yeqiu Li, Takashi Yahagi Yahagi, Lu & Sekiya Lab., Noise Removal for Medical X-ray images in Multiwavelet Domain, 2006 International Symposium on Intelligent Signal Processing and Communication Systems(ISPACS 2006), Yonago Convention Centre, Tottori, Japan 8. Ling wang, Jianming Lu, Yeqiu Li, Takashi Ya Yahagi, Lu & Sekiya Lab., Takahide Okamoto Noise Reduction Using Wavelet with Application to Medical X-ray Image / IEEE 9. C. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," IEEE Trans. Image Processing, vol. 9, no. 9, pp ,