Keywords Medical scans, PSNR, MSE, wavelet, image compression.

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1 Volume 5, Issue 5, May 2015 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Effect of Image Compression on Medical Scans Using Wavelet Transform Rahul Samnotra *, Randhir Singh Department of ECE, Sri Sai College of Engg & Technology, Pathankot, Punjab, India Abstract In this work, image has been compressed using different wavelet transforms of wavelet compression technique with different levels of compression. Medical scans were taken and on these images different wavelet compression techniques were implemented. The parameters of the image were calculated with respect to the original scans. Peak signal to noise ratio (PSNR) and mean square error (MSE) of the decomposed images were calculated. PSNR is used to measure the difference between two images. From the several types of wavelet transforms, Haar, Daubechie (db), symlet, and coiflet wavelet transforms were used to analyze the results. The value of threshold is rescaled for denoising purposes. De-noising methods based on wavelet compression is one of the most significant applications of wavelets. Keywords Medical scans, PSNR, MSE, wavelet, image compression. I. INTRODUCTION The most common method of identifying and isolating an image into regions is by thresholding in which all pixels having intensity values above or below some level. Thresholding is an integral part of converting an intensity image to a binary image [1]. The basic concept of thresholding can be extended to include both upper and lower boundaries of an image. Image quality has different conventional definition depending on the context or application in which it is being used. One of the major applications regarding image processing is to enhance the quality of medical scans for observing different human body parts effectively [2]. Medical scans captured from different instruments like X rays, Magnetic Resonance Imaging (MRI), computed tomography (CT) scans, and ultrasound are not always very clear [3-6]. Hence, image processing is required to quantify the quality of medical scans. Image processing involves image formation, reconstruction, compression, enhancement, and display for measuring image quality. In this paper, the main concerned with image quality is for image compression, where quality is measured relative to an original and uncompressed image [7-9]. Measures used to evaluate image compression schemes are still primarily limited to the mathematical functions, such as MSE, peak signal-to-noise ratio (PSNR). The wavelet transform is one of the most powerful techniques for image compression. Various levels of different wavelet transformations were exploited to evaluate the optimum results. An algorithm is developed to compress the unclear original image [10]. These coefficients are then denoised with wavelet threshold. Finally, inverse transform is applied to the coefficients and get denoised image In this paper, wavelet decomposition technique is applied to compress the input image for enhancing the quality. A specific threshold value is determined to enhance the quality of an original image. II. METHODOLOGY In this experiment, four different wavelet compression techniques have been taken into account to enhance the quality of an original image. The input image is compressed at different depth levels with haar, daubechie (db1) wavelet, symlet, and coiflet wavlet. The steps taken into consideration are shown in a sequential manner in the form of the flow chart as in fig. 1. From the flow chart, it is simple to explain that the original input image is first compressed. In a program, the image is read in the form of matrix and the image is compressed upto n different levels. For this experiment, the value of n is selected as 5. The threshold value is selected for different sub-band to determine the scale parameter. Soft thresholding is applied to sub-bands to reconstruct the original image. The final step is to calculate the image parameters like PSNR and MSE. PSNR is used to measure the difference between two images. It is defined as b PSNR 20log 10 rms where b is the largest possible value of the signal (typically 255 or 1), and rms is the root mean square difference between two images. The PSNR is given in decibel units, which measure the ratio of the peak signal and the difference between two images [11][12]. An increase of 20 db corresponds to a ten-fold decrease in the rms difference between two images. 2015, IJARCSSE All Rights Reserved Page 1062

2 Start Loading input image Applying different wavelet transformation Use different levels Synthesized image Calculating PSNR & MSE Comparing different wavelet transformations Stop Fig. 1 Work flow showing procedural steps used in experiment. here are many versions of signal-to-noise ratios, but the PSNR is very common in image processing, probably because it gives better-sounding numbers than other measures. Mean squared error (MSE) of an estimator is one of many ways to quantify the difference between values implied by an estimator and the true values of the quantity being estimated [13]. III. RESULTS AND DISCUSSIONS The results that can be obtained from different wavelets depends on the image and level used. This is because different wavelets look at the energy changes in the image differently. Fig. 2 shows the original input image that has to be processed using wavelet transformation. In the orthogonal wavelet compression procedure, the threshold is set to , IJARCSSE All Rights Reserved Page 1063

3 Fig. 2 Original and Processed images using Haar wavelet transformation. Fig. 3 Original and Processed images using Daubechie wavelet transformation. 2015, IJARCSSE All Rights Reserved Page 1064

4 The first set of results used the simplest combination which was to use specific thresholding. The haar wavelet was then decomposed upto level 5 and is shown in fig. 3. The results showed a clear pattern, in that decomposing the images to greater levels increased the compression but reduced the energy retention. The results suggest that a better compression rate had been gained by simply analysing at a deeper decomposition level without the need for thresholding. Even though the coefficients.the higher the decomposition level, higher is the percentage of zeros obtained with no thresholding. This is because decomposing to greater levels means that a higher percentage of coefficients come from detail subsignals. Detail sub-signals generally have a smaller range of values than the approximation sub-signals, ideally zero values. Therefore this pattern shows that as decomposition level increases, more detail is filtered out with value zero. Then the next step consists of increasing the level approximation coefficient vector, successive details are never reanalyzed. Figure 3 shows the compressed image at different depth levels with daubechie (db1) wavelet. In the corresponding wavelet packet situation, each detail coefficient vector is also decomposed into two parts using the same approach as in approximation vector splitting. This offers the richest analysis. The complete binary tree is produced in the one-dimensional case or a quaternary tree in the two-dimensional case. Figure 4 shows the synthesized image using Symlet wavelet transformation. Fig. 4 Original and Processed images using Symlet wavelet transformation Soft thresholding shrinks coefficients above the threshold in absolute value. Figure 5 shows the compressed image after applying Coiflet wavelet transformation. 2015, IJARCSSE All Rights Reserved Page 1065

5 Fig. 5 Original and Processed images using Coiflet wavelet transformation. TABLE I VALUES CALCULATED FOR OBTAINING PSNR AND MSE IN TABULAR FORM. Level Haar Daubechie Symlet Coiflet PSNR MSE PSNR MSE PSNR MSE PSNR Level Level Level Level Level Level Level Level Level Level Finally, the PSNR and MSE are calculated and is shown in table 1. On comparing the evaluated outputs, results showed that the value of MSE is minimum at level 1 for haar and db wavelet technique while for Symlet and Coiflet, it shows minima at level 5. The MSE and PSNR were calculated in-between the original input image and that of the output reconstructed image. The same trend is observed for the values of PSNR. Among all the wavelet transforms that are tested for this experiment, the results showed that Daubechie wavelet has better performance while compression. 2015, IJARCSSE All Rights Reserved Page 1066

6 IV. CONCLUSION From the obtained results, this can be concluded that the compression of digital input medical scans using daubechie wavelet is effective for image processing. An input digital image is processed using daubechie (db) upto depth of level 5. Analysis shows that half of the interpolation factor was used for exclamation of the different frequency subbands. During compilation, the compressed image has been analysed to generate a super resolved image. The proposed experiment shows good and economical compression method while calculating PSNR and MSE. A visual result confirms that the proposed technique is better than the conventional and image compression technique. REFERENCES [1] Hubbard, Barbara Burke. The World According to Wavelets. A.K Peters Ltd, [2] H. L. Resnikoff and R.O. Wells, Jr., Wavelet Analysis The Scalable Structure of Information, Springer ñverlag New York, Inc [3] Parveen Lehana, Swapna Devi, Satnam Singh, Pawanesh Abrol, Saleem Khan, Sandeep Arya, Investigations of the MRI Images using Aura Transformation, Signal & Image Processing : An International Journal (SIPIJ), vol.3, no.1, pp , February [4] Sandeep Arya and Parveen Lehana, Development of Seed Analyzer using the techniques of computer vision, International Journal of Distributed and Parallel Systems (IJDPS), vol.3, no.1, pp , January [5] Sandeep Arya, Saleem Khan, Dhrub Kumar, Maitreyee Dutta, Parveen Lehana, Image enhancement technique on Ultrasound Images using Aura Transformation, International Journal in Foundations of Computer Science & Technology (IJFCST), vol.2, no.3, pp. 1-10, May [6] H. Om, M. Biswas, An improved image denoising method based on wavelet thresholding, J. Signal Inf. Process, vol. 3, no. 1, pp , [7] F. G. Meyer, A. Z.Averbuch, and J. O. Strömberg, Fast adaptive wavelet packet image compression, IEEE Trans. Image Processing, vol. 9, pp , May [8] K. Ramchandran and M. Vetterli, Best wavelet packet bases in a ratedistortion sense, IEEE Trans. Image Processing, vol. 2, pp , Apr [9] S. Zhao, H. Han, and S. Peng, Wavelet domain HMT-based image super resolution, in Proc. IEEE Int. Conf. Image Process., Sep. 2003, vol. 2, pp [10] G. Anbarjafari and H. Demirel, Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image, ETRI J., vol. 32, no. 3, pp , Jun [11] S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. New York: Academic, [12] Battula.R.V.S.Narayana and K.Nirmala, "Image Resolution Enhancement by Using Stationary and Discrete Wavelet Decomposition," International Journal of Modern Engineering Research, Vol. 2, Issue. 5, pp , [13] R.A. Devore and B. J. Lucier, Wavelets, in Acta Numerica l, Cambridge University Press, London, pp. 1-56, , IJARCSSE All Rights Reserved Page 1067

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