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1 Noise Reduction in Medical Imaging - Comparison of noise removal Algorithms - H. OULHAJ LRIT Unité Associée au CNRST (URAC 29) MOHAMMED V University Rabat, Morocco hind.oulhaj@gmail.com A. AMINE 1, M. RZIZA 2 And D. ABOUTAJDINE 2 1,2 LRIT Unité Associée au CNRST (URAC 29) MOHAMMED V University, Rabat, Morocco 1 ENSA, Ibn Tofail University, Kenitra, Morocco amine_aouatif@univ-ibntofail.ac.ma, rziza@fsr.ac.ma Abstract The Medical community uses several image acquisition techniques for diagnosing and suggesting the corresponding therapies. Therefore the obtained images from clinical examinations should be treated to assist doctors in results interpretation. In this paper, we focus on denoising task in order to determine the benefits and drawbacks of each denoising algorithm. For this, we used as data set the most common acquisition tools namely: Magnetic Resonance (MR), Computed Tomography (CT), Ultrasounds, Scintigraphy and X-Ray images. For measuring the denoised image quality we used : Signal to Noise Ratio (SNR), Peak to Signal noise (PSNR) Root Mean square Error (RMSE) and the Mean Structure Similarity Index (MSSIM). Keywords-component; Medicals Images; denoising; Speckle Noise; Rician Noise; Poisson Noise; Additive White Gaussian Noise (AWGN); Nl-Means, Fast Nl-Means, Total Variation, Anisotropic Diffusion, Wavelet Coefficients Thresholding I. INTRODUCTION Nowadays, medical staff requires a best medical images quality for achieving efficient and fast diagnosis. Indeed the obtained images from different medical acquisition techniques are blurred and usually corrupted by noise. For these reasons, many studies has been done and gave as resulting output several denoising methods which can be categorized in two classes. In the first category the original image is transformed in frequency or wavelets domain then removing noise methods are applied as: Hard or Soft wavelet thresolding coefficients [1]. In the second category the unwanted noise is directly suppressed in spatial domain. Among these denoising algorithms we can name: Perona and Malik approach known as Anisotropic Diffusion [2] based on Partial Differential Equation (PDE) which attempts to save as much as possible edges and lines in images. Total Variation approach, which goes back to Rudin and Osher [3]. The denoising process take into account the minimization of an energy function that will be detailed in the next sections. The wavelet Thresholding technique was introduced by Weaver [4] and it s one of the most discussed techniques in denoising field. It's easy for implementation because noise is represented by the smallest coefficients in the high frequencies. Setting these coefficients to zero removes more noise in the image but the Threshold should be chosen adequately to ensure an efficient denoising. Pizurica and Phlipps algorithm [5] uses a multiresolution denoising approach based on Wavelets which estimates noise in each noisy image detail while allowing doctors progressive noise reduction. NL Means [6] discovered by Buades takes into account the redundancy of the information in the image. This method overcomes the commonly artifacts occurring after using neighborhood filters [7]. In this paper, another version of Nl-Means is used to compare different denoising techniques. It s faster than Nl-Means basic version. We compare the mentioned algorithms above to Homomorphic Wiener, Median and Bilateral filters. This paper is organized as follows: Section II gives an overview of denoising approaches used in medicine for: MRI, Ultrasound, CT-Scan [8], Scintigraphy [9] and X-Ray data. Section III describes a comparative study providing merits and demerits of each approach. Finally, discussions and future directions are drawn in Section IV. II. NOISE REMOVAL TECHNIQUES Various algorithms for medical images noise removing have been proposed mainly: NL-Means and Wavelets Theory, which have been tested successfully for different medical imaging data. This section describes the most widely used approaches in denoising field to reduce noise for: MRI, Ultrasound, CT and Scintigraphy data. We have chosen to apply the most commonly noises found in each medical modality as: Speckle noise for Ultrasound case, Rician noise for MRI case, AWGN for CT image and Poisson noise for Scintigraphy images. Finally, for better Assessing denoising results, we have tested the same approaches for noisy dental X- Ray images. A. Speckle Noise reduction for Ultrasound data Speckle noise is commonly found in Ultrasonic and Radar images. When such a noise is present in the obtained images, the goal idea is to remove it while preserving as much as possible the important image characteristics, because reducing Speckle noise may be counterproductive in several cases. For example we can easily lost tracking features in Ultrasound. The first apparition of Speckle removing algorithms (Lee or Forst filter and Kuan filter [10]) was done by SAR (Synthetic Aperure Radar) researcher s community because Speckle noise affects all coherent imaging system including SAR systems. Recently, most of denoising approaches are based on Wavelets Thresholding or Partial Differential Equations PMI 16/09 Project supports this work

2 (PDEs). In Wavelet domain, the obtained image is divided in coefficients through scales and the goal of filtering is to better estimate the noise free coefficients. However in based PDE algorithm the main idea is to minimize functions as in (1), which describes a constrained minimization problem of Total Variation method. R represents the free noise image, Y the image that should be restored and the Gradient Operator. Arg Min ψ(x)= R(x)dx +λ Y(x) R(x)dx * Y(x) R(x)dx dx (1) The Total Variation Method has offered promising results. Indeed it has proved that it conserves straight edges however the finer images details can be lost after the denoising process. Figure 1. Data Set of test Images B. Rician Noise reduction for MRI data As Ultrasound images, the MRI data are often affected by a non-additive noise, which is Rice noise [11] in MRI data case. Wavelet Thresholding have been applied to denoise MRI images. Non Local Means is also one of the strongest Schemes for MRI noise removal. Several variations have been brought to the NL-Means basic version of Baudes [6], one of theme is the Adaptive NL-Means Scheme [12]. The described Approaches in Section II.A are used too for MRI noise reduction. C. White Additive Noise reduction for CT data One of the most used modality in medical field is a CT image. However the produced images are corrupted by AWGN noise. Since it is additive noise, different classical filters have been applied to reduce it, mainly both Wiener and median filters. The most recently research in CT images denoising approaches are based on Wavelets and Multi-Wavelet Transformation and Thresholding [13] which includes three processing steps namely: Multi-wavelets decomposition then Thresholding, reconstruction and Enhancement. In addition, others multiscale geometric transforms like Curvelet transform [14] have been successfully applied to CT Noise reduction. D. Poisson Noise Reduction for Scintigraphy data Scintigraphy images like others modalities contain important and useful information that can be needed in diagnosis. However unlike others medical images, the Scintigraphy or functional MRI data are often corrupted by Poisson noise. An efficient NL-Means approach [15, 16] has been specifically developed for Poisson removal. III. COMPARATIVE STUDY OF DENOISING APPROACHES At the beginning we affect data set (presented in Fig. 1) with various noises distributions for different noises levels, then we applied several noise removal approaches, which have been seen in previous sections. Next, the efficiency of denoising algorithms is, experimentally, evaluated in terms of SNR, RMSE and MSSIM. The implementation process steps are described in Fig. 2. Figure 2. Implementation Process A. Denoising results for Synthetic data This section describes the obtained results of denoising process for the Synthetic image a. in Fig. 1. Fig. 3 shows the obtained denoised image by the various approaches seen in previous Sections. We have speckled the original synthetic image by σ 2=0.4 of noise. For Total Variation method we stopped at the 20 iteration, for Pizurica s approach we have set the K parameter at 4. This parameter controls the quality of denoised image. The chosen window size is: 5*5. Visually we can see that three approaches, which are : Nl-Means, Fast Nl- Means and Wavelets Thresholding, didn't damage the sharpness and the denoised image features. Nl-Means and Fast Nl-Means algorithms perform good visual results because it takes in account all images pixels. Indeed it replaces the intensity of each pixel in noisy image by a weighted average of all pixels intensities. For Wavelet process we have applied an adaptive thresholding. The selected threshold is one that returns the best PSNR or SNR improvement. Wavelet decomposition demonstrates high efficiency because it gives a better exploration of image details. For other methods the Speckle noise is hardly removed and the denoising process performs a snowy and blurred image. Fig. 4 shows that both Wiener and Median Filters are not really efficient for removing a multiplicative noise because they were made originally to remove only additive noise.

3 All this results prove strong noise suppression with NL- Means algorithm for the synthetic image. We will check, in the next sections, whether this conclusion is valuable for real medical data. Figure 5. Summarized results of image denoising performance under Speckle noise: SNR computed against different noise sigma values Figure 3. Perceptual Quality comparison of various denoised images Figure 4. From left to right: Denoised image by both Wiener and Median filters (window used: 3*3) From the shown results in Tab. I we note that Bilateral Filter, Total Variation, Nl-Means and Fast NL-Means provide a higher MSSIM value which mean that they keep the image structures unchanged after denoising process. However the higher value of SNR is resulting only by both: basic and Faster Nl-Means approaches. TABLE I. THE OBTAINED SNR, PSNR, RMSE AND MSSIM MEASURES BY DIFFERENT SPECKLE REMOVAL METHODS(σ 2=0.4) Method SNR PSNR RMSE MSSIM (in db) (in db) (in db) Bilateral Filter Anisotropic Diffusion Wavelet Pizurica and Philips Total Variation Nl-Means Wiener Filter Median Filter Fast Nl-Means We have corrupted the original image with several noises levels in order to observe the noise impact on the image on one side, and the efficiency of noise removal approaches in term of SNR on the other side. According to Fig. 5 Nl-Means yields a significant SNR gap over other denoising methods. In terms of RMSE, Fig. 6 shows that the smallest value of RMSE is obtained by Nl-Means approach too. Figure 6. Summarized results of image denoising performance under Speckle noise: RMSE computed against different noise sigma values B. Denoising results for Ultrasound data The second level of experiment is dedicated to denoising a leg vein ultrasound image, which is b. in Fig. 1. In this case we have chosen to affect the ultrasound test image by various levels of Speckle noise, which is the most common noise founded in Ultrasound modality. At the beginning we affect the original ultrasound image with a very important noise level (the σ2 used is equal to 0.8). Fig. 7 shows obtained result it indicates that all the approaches except Nl-Means and Fast Nlmeans fails in denoising process because of the high level value of noise presents in the image. In fact Tab. II shows that both Nl-Means and Fast Nl-Means outperform the other denoising process in term of the SNR measure. In addition, if we refer to the Tab. III, we note that the Nl-Means algorithm gives a good MSSIM output result. The MSSIM value remains good (close to 1) even if an important noise level corrupts the tested image. In the next experiment with the same image we restricted the comparison of the denoising approaches to Nl- Means and Nl-means fast. We varied the sigma between 0.2 and 0.7. The denoised images are displayed in Fig. 8 and Fig. 9. From these figures, the NL-Means and Nl-Means fast show generally superior performance. But actually, it s not evident to judge the performance of these approaches by visual inspection only, or even by SNR resulting because a doctor's knowledge is needed to confirm that point.

4 Figure 7. Perceptual Quality comparison of various Despeckled Methods {σ 2=0.8} Figure 8. Perceptual Quality Comparison of various Despeckled images { σ 2= 0.2; σ 2= 0.3; σ 2= 0.4} We conclude from these results that the Nl-Means provides a strong Speckle noise reduction, but it requires an important time for processing comparing to other methods. However time processing is crucial especially in Medical context. TABLE II. COMPARISON OF SNR OF DIFFERENT DENOISING APPROACHES FOR ULTRASOUND IMAGE CORRUPTED BY SPECKLE NOISE σ 2 Approaches Nl-Means Nl-Means Fast TV AnisotropicDiffusion Wavelet Pizurica Bilateral Filter Median Filter TABLE III. COMPARISON OF MSSIM OF DIFFERENT DENOISING APPROACHES FOR ULTRASOUND IMAGE CORRUPTED BY SPECKLE σ 2 Approach NL-Means Figure 9. Perceptual Quality Comparison of various Despeckled images { σ 2=0.5; σ 2=0.6; σ 2=0.7) C. Denoising results for MRI data Generally, the noise distribution occurred in MRI data is Rician one which is a multiplicative noise. In this section the test images are: d. and c. form Fig. 1, respectively, Sagittal and Axial brain data. The obtained results in previous sections leads us to apply both Nl-means versions to MRI data in order to determine if the quality of the denoised image will be maintained when changing the noise distribution.

5 TABLE IV. COMPARISON OF MSSIM OF NL MEANS AND NL MEANS FAST FOR MRI IMAGE CORRUPTED BY RICIAN NOISE σ 2 Approaches NL-Means NL-Means Fast The visual inspection is in favor of Nl-means algorithm. For σ 2=60 level, Fast Nl-means fails, however NL-means performs a strong removing noise until damaging the image structures. The result in Tab. IV shows that the Fast NL-mean algorithm success in recovering the original image structures even if the denoised image is not visually good. In the next example we have studied the denoising effect in MRI data for several noises distributions. We tested in this case all the denoising approaches described before. Figure 12. Summarized results of image denoising performance under Rician noise: RMSE computed against different noises sigma values output. Next, we test the second MRI image (image c. in Fig. 1). Results are displayed in figures 13 and 14. Figure 13. Perceptual Quality Comparison of various Rician removal algorithms applied to MRI data (σ 2=0.6) Figure 10. Perceptual Quality Comparison of Rician removal algorithms: Nl- Means and Fast Nl-Means Figure 14. From left to right: the original test image, noisy image (σ2=0.6) denoised image by: Wavelet Thresholding, Wiener and Median Filter. Appling Pizurica s approach and Anisotropic Diffusion to MRI data gives lower SNR comparing to Nl-mean SNR. Also visually, we note that the image given by Nl-means algorithm is better than the given image by other approaches. But again we cannot confirm whether the denoised image by NL-Means would be useful for doctors. Figure 11. Summarized results of image denoising prformance under Rician noise: SNR computed against different noises sigma values Results in Fig. 11 and Fig. 12 show that Nl-means s method gives the lowest value of RMSE and the higher SNR D. Denoising results for CT and Scintigraphy data Based on previous Sections results, we have found that the NL-Means is better than any other approach. Indeed we can notice a good visual result for both Ultrasound images and MRI data. In this section we tested this algorithm to dental CT data. Last years, medical staff uses more and more CT data.

6 Figure 15. Perceptual quality comparaison of various poisson removal algorithms applied to Scintigraphy data unfortunately, test results are often corrupted by Gaussian additive noise. This noise reduces the medical staff ability to give an accurate diagnosis. The Poisson noise is considered as a complex distribution of existing noises and only a few filters are able to remove it. In our experience we have applied as mentioned in Fig. 15 four main approaches. Next we have affected a dental image with a Speckle noise. Figures 16 and 17 display results. From these figures we can note that the NLmeans outperform other denoising techniques and also that the classical Wiener and Median Filters fails in the denoising process. Figure 16. Perceptual quality comparison of various Speckle removal algorithms applied to Dental X-Ray data Figure 17. From left to right : the original test image, noisy image (σ 2=0.4 of Speckle Noise), denoised image by : Wavelet Thresholding, Wiener and Median filters IV. CONCLUSIONS In Medicine, doctors are faced to the problem of recovering images from noisy and incomplete produced images. Hence a denoising process should remove artifacts and noise. Generally it s difficult to suggest an efficient noise removing method while conserving anatomical details and for all medical images modalities. The shown results in Section III proved a strong removing noise for the Nl-Means algorithm because it uses all pixels in the denoising process. However this approach requires much time compared to others approaches. For this reason we have tested another version of NL-Means, which is faster, but the testing results show that the basic Nl-Means remains better than the faster version. Our future work will focus again on medical images denoising field. We will try to reduce the time required by the algorithm while retaining its denoising characteristics. Especially we will look to validate the results with a medical expert. Another research direction is to work on the classification of medical images, which also requires a pretreatment such as denoising. [1] P. Bao and L. Zhang, 2003, Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding, IEEE Trans on Medical Imaging. Vol. 22, pp [2] Pierto Perona and Jitendra Malik, Scale-Space and Edge Detection using Anisotropic Diffusion. In IEEE Trans. Vol. 12. [3] L.I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms. PHYSICA D, Vol. 60, pp [4] Weaver JB, Xu Ys, Healy DM Jr, Cromwell LD, Filtring noise from images with wavelet transforms, Magn Reson Med. Vol. 21, pp [5] Aleksandra Pizurica and Alle Mejie Wink, A review of wevelt denoising in MRI and Ultrasound brain imaging Bentham Science Publishers, Vol 2, pp [6] Antoni Buades, Bartoum Coll, A non-local algorithm for image desnoising, IEEE Trans. [7] J.S. Lee. Digital image smoothing and the sigma filter, Computer Vision, Graphicsand Image Processing. pp [8] PFAU Patrick R, SIVAK Michael V. and CHAK Amitabh, Criteria for the diagnosis of dysplasia by endoscopic optical coherence tomography, Gastrointestinal endoscopy. Vol. 58, pp , [9] Michael V. Green, Harold G. Ostrow and Margaret, High temporal Resolution Ecg-Gated Scintigraphic Angiocardiography. JNM. Vol. 16, pp [10] D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, Adaptive restoration of images with speckle, IEEE Trans. Speech, Signal Processing, vol. ASSP-35, pp [11] J. Sijbers, A.J. den Dekker, M. Verhoye, J. Van Audekerke, and D. Van Dyck, 1987.Estimation of noise from magnitude MR images, Magn Reson Imaging, pp [12] T. Thaipanich, C.Jay Kuo. An adaptive Nonlocal Means Scheme for Medical Image Denoising, Proceedings of the SPIE Vol 7623, pp M-76230M-12. [13] S. AMJAD, S.VATHSAL, K. Lal Kishore, An Efficient Denoising Technique for CT Images using Windouw Based Multi- Wavelet Transformation and Thersholding. European Journal of Scientific Research ISSN X Vol.48 No 2. pp [14] R.Sivakumar, 2007 Denoising of Computer Tomography Images using Curvelet Transform. in ARPN Journal Of Engineering and Applied Sciences [15] C.A. Deledalle, F. Tupin, Denis, Poisson NL Means Unsupervised : Non Local Means for Poisson Noise, IEEE Conf. on Image Processing. [16] I. Rodrigues,J. Sanches, J. Bioucas-Dias, Denoising of Medical Images corrupted by Poisson Noise. Image processing. ICIP 15th IEEE International Conference. pp

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