See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/347246640 Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey Article in Current Medical Imaging Reviews · November 2020 DOI: 10.2174/1573405616666201118122908 CITATION READS 1 156 3 authors: Ali Arshaghi Mohsen Ashourian Islamic Azad University Central Tehran Branch Islamic Azad University 12 PUBLICATIONS 23 CITATIONS 73 PUBLICATIONS 312 CITATIONS SEE PROFILE SEE PROFILE Leila Ghabeli Sharif University of Technology 32 PUBLICATIONS 117 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: machine learning View project Image Transmission in UAV MIMO UWB- OSTBC System over Rayleigh Channel Using Multiple Description Coding (MDC) View project All content following this page was uploaded by Ali Arshaghi on 12 May 2021. The user has requested enhancement of the downloaded file. Send Orders for Reprints to reprints@benthamscience.ae Current Medical Imaging Reviews, 2018, Volume 1 Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A survey Ali Arshaghi a, Mohsen Ashourian*, b, Leila Ghabeli a a b Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran Department of Electrical Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran Abstract: Several Denoising Methods for Medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods. In this paper, A median filter algorithm will be modified and explained the image denoising to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn-CNN), Gaussian noise, and Salt and pepper noise used in the Medical skin image. PSNR values of the CNN method have higher and better than to different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). Denoising methods performance with indices SSIM, PSNR, and MSE tested, and the results of simulation image denoising also have been presented. Keywords: Medical Denoising, NLM, PSNR, Image Processing, CNN, Adaptive Wiener Filter. 1. INTRODUCTION Researchers want to find a way scientific treatment for diagnosing, detecting, and predicting diseases, and therefore radiologists are attracted to medical data mining for patient care. Medical image analysis [1-4] is a subject area of interest. Medical data mining and image denoising is a sample of challenges. Image denoising uses in the pre-processing step in medical image processing, and It is a Main pre-processing step. Different algorithms propose for denoising. Recently, Deep Learning Networks have been using. These methods are limited to Large training data set and high computational costs. In industrial work, the image has a vital part in the information-carrying system. The noise corrupts the image interacts with it during its acquisition or transmission, and other causes include hardware faults in the camera lens, lesser processing power, Etc, which quality image has decreased, not soothing to the human eye. Therefore, Denoising is the main problem and considerable in image processing. Image denoising is a field in the image processing Domain which removing the noise in the image along with the preservation of image details. In the last decade, research done on image Denoising in the medical field. Melanoma is the deadliest type of skin cancers, originates from skin pigmentation cells, melanocytes [1]. It is the 15th most commonly occurring cancer in humans. Satellite imaging and forensic laboratory images to produce *Address correspondence to this author at the Department of Electrical Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran; cell phone: +989131269782 E-mail: ashourian@iaumajlesi.ac.ir XXX-XXX/15 $58.00+.00 more fine and clear pictures as per application. Image denoising using different techniques such as Wavelet Transform, CNN, linear and Non-linear method. Machine Learning techniques use the diagnosis, disease detection, and disease prognosis, and this field is suitable. Get better diagnostic accuracy. Therefore, design systems had used for breast tumor detection and classification, fetal development and growth, brain functioning, skin lesions, and lung diseases [5]. Noise in the image is undesirable in digital image processing, and the work process is the challenge. A major fundamental problem in image preprocessing is removing each noise, such as Gaussian noise and Rayleigh noise. These noises produce in the image sampling time or transmission in wireless channels. Image denoising is an obscurant problem in image processing and computer vision, in which researchers work in this field. Transform based, such as discrete wavelet (DW) [24], Shearlet [5], curvelet [6], discrete cosine (DC) [7], isotropic diffusion filtering [8], bilateral filters [9] are used in image denoising. In this paper, a median filter is modified and investigate for image denoising wavelet transform and Non-local means (NLM), deep convolutional neural network (DnCNN). Gaussian and Salt and pepper noise used in the skin image. Median Filter (MF) and Adaptive Median Filter (AMF) and Adaptive Wiener filter (AWF), and Wiener Filter (WF) use in the tests. The filters that apply for denoising are skin images, Performance, and efficiency of each filter measure with different noise densities. Performance of used filters compared to SSIM, MSE, RMSE, Peak Signal-to-Noise Ratio (PSNR) [10, 11]. In the end, the best filtering for denoising skin image propose. © 2017 Bentham Science Publishers Title of the Article Journal Name, 2019, Vol. 0, No. 0 2. Image Denoising Techniques Some different medical image de-noising procedures are such as Weiner Filter, Adaptive Wiener filter, Non-linear filters. This section of the paper describes these techniques. 2.1 Weiner Filter Various filters are defined for noise removal and image restoration. The most popular of these filters is the Wiener filter. The Wiener filter has a statistical look at processing. The filter assumes that the image and noise have a Gaussian distribution with a mean of zero. The Wiener filter operates in order to reduce the mean square error 2 between the original image I original and the approximate image ( I denoised ) from the noise image. I original I denoised 2 2 (1) The Wiener filter has highly capable of eliminating Gaussian noise, but as stated, this filter aims to minimize the sum of mean square error for whole the image, and therefore acts the same on the image for different regions. Whereas the possibility of human sighting in all areas image is not the same; the reconstruction process should be more in uniform light areas or in high brightness areas than to high gradient areas or dark areas. Also, if the damage has occurred in different areas of the image in different forms, it will not be possible to reconstruct it with the Wiener filter [12]. 2.2 Adaptive Wiener Filter One of the filters used in the frequency domain is Adaptive Wiener Filter (AWF). This filter work with the statistical characteristics of the image and the Maximum rectangular window. Mean and, Variance is two main parameters that play a Main role in makes adaptive filters. Adaptive filter performance is better than non-adaptive filters [13]. In some cases, filters use to adapt to the properties of different areas of the image. Adaptive filters are one of these types of filters, which are filtering according to the noise level of various area images. The Base work of this filter statistics estimated from each neighborhood pixel image, find an estimated local mean and standard deviation image, function the adaptive Wiener filters use the neighborhoods each pixel which it is local and each neighborhood pixel is computed separately for all pixels’ image. Figures of the result section and simulation show the use of this filter in a noisy image. As we can see, the resulting image has less blur than the mean filter [14]. 2.3 Non-linear filters Another type of image processing filter is the NON-Linear filter. When using linear filters, all pixels’ image is affected by the coefficients. Non-linear filters use to avoid this problem. In this type, we considered the value of a neighborhood pixel based on the specific property as the Central pixel value. For example, the maximum neighborhood value is considering as the central value. Non-linear filters surveyed in [15, 16]. 2 In the next sections, the median filter and the adaptive median filter will explain. In the non-linear filtering approach, the noise image removes, without any effort to identify it distinctly. The Nonlinear filtering has a low pass filtering on the image. This filter removes noise blurring images results in the edges in pictures invisible. The median filter is an example of a non-linear filter [17]. The principle median filtering is the output pixel value does specify by the mean of the neighborhood pixels. The median filter is less sensitive than the mean filter to the outliers (values extreme in the image). So median filtering is the best approach to eliminate the outliers without deducting the sharpness of those images. This filter moving window does implement with a convolution mask or kernel, usually 3× 3, 5×5, or 7×7 window or square kernel. The median of a window does calculate and, then this value replaced the Center pixel value of the window. 2.4 The proposal median filter The Median Filter is the filter to eliminate noise in images. In this filter, the square windows can consider as (2k + 1) × (2k + 1) or as a cross and, its center of gravity ss shifted to sporadic pixels of the image. So the values inside the window are arranged from small to large, and then the median value is the central gray level value. g (m, n) median f (m k , n k ) f (m k , n k ) f (m.n) f (m k , m k ) (2) f (m k , n k ) The median filter has a Good capability to eliminate impulse noise. But this filter has not capable of reducing Gaussian noise well. Another benefit of the mentioned filter is that it does not create a new Amount of brightness (Gray Area) image. Its major weakness is in moving the edges in the image as much as one or two pixels, and if the size window in the filter increased cannot the noise be eliminated also the resolution of the small surfaces non-noise of the original image eliminate and, the image tilted to an artificial the image. 2.5 Adaptive Median Filtering The Adaptive Median Filtering (AMF) [18] is an image de-noising method and, using in the noisy image. In this method, each pixel in the images computed with the neighbor pixels. Adaptive filter performance is better at keeping sharp detail images. Some details that were distorted or blurred and unrecognizable by applying the median filter were Sharp and better. The adaptive median filter acts on a rectangular window with area S xy . Unlike other filters, this filter changes the Size of the window when applying a filter based on a particular condition. Each time the algorithm gives some value as outputs, the S xy window moves to the next location in the image, then the algorithm restarts and applies to the new location pixels. The adaptive median filter acts in two levels Title of the Article Journal Name, 2019, Vol. 0, No. 0 that defined Level A and Level B as follows. L11 Z med Z min Level 1: L12 Z med Z max if L11 0 AND L12 0, Go to level 2 Else increase the window size. If window size S max repeats level 1. Else output Z xy . Level 2: L21 Z xy Z min L22 Z xy Z max if L21 0 AND L22 0, output Z xy Else output Z m ed . n( x, y ) f ( x, y ) g ( x, y) n( x, y ) f ( x, y ) g ( x, y) (3) 2.6.2 Gaussian noise Gaussian noise is one of the most Important noises that degrades the image quality that is distributed evenly over the image [19, 20]. Output noisy image every pixel is the sum of a random Gaussian Distributed noise value and image pixel value. For the simulation in MATLAB software, Gaussian noise uses the Gaussian White Noise with mean m and variance v to the input image. It refers to as Gaussian distribution. The probability density of the Gaussian noise is the same as the normal distribution [21]. Gaussian noise has a corrupted part in the whole image and is associated with a Gaussian function. Gaussian noise (also called normal) use in operation because of its mathematical traceability over time and frequency domains. This capability is so reliable that it even uses when it is only cross-border. The probability density function (pdf) of a Gaussian random variable z is obtained from Equation 4 and shown in Figure 1: 1 p( z ) e 2 Where 3 ( z )2 2 2 (4) Z min is a minimum gray level value in S xy . Z max is a maximum gray level value in S xy . Z m ed is a median of gray levels in S xy . Z xy is a gray level at coordinates ( x, y) . These three above parameters are gray level. Smax is a maximum allowed size of S xy . The output of the filter is a quantity that replaces to the value pixel at ( x, y) , the point which Central of S xy coincide it. 2.6 Common noises in Medical image Gaussian noise and Salt and pepper noise are Popular noises distributed in medical images. Some filters use in the papers and researches to remove the Gaussian and Salt and pepper noises. This noise is produced to the thermal noise of the patient or through the acquisition hardware. In this section, two type noise model is defined and, survey these noises on the medical images. 2.6.1 Noise Model If assume g ( x, y) f ( x, y) is the original or input image and is the noise that adds to the original image. Then noise image represented as n( x, y) and ( x, y) denotes the pixel location in the Images. Noise present either Additive and Multiplicative. The Additive and Multiplicative noise represent as (3) Respectively. Fig. (1). Gaussian noise That P(z) is the Gaussian distribution and, μ represents the brightness intensity of the mean value of z and σ standard deviation. The standard deviation squared is named the variance z. Use a mask is an effective way to eliminate Gaussian noise that moves over the image and Mean of neighbor places in the Midpoint at each Step. Use a Mask is an effective way to Eliminate Gaussian noise that moves over the image and Mean of neighbor places in the Midpoint at each Step. 2 2.6.3 Salt and Pepper Noise The probability density function of the impulse noise (dipole) is as follows: pa for z a p( z ) pb for z b 0 otherwise (5) If a> b be, the brightness b is a bright spot, and the surface a, is a dark spot in the image. If either pa or pb are zero, the Title of the Article Journal Name, 2019, Vol. 0, No. 0 noise of the resulting multiplication is called a polarization. If neither is zero (especially if both are approximately equal), the Resulting noise will be similar to the dispersion of salt and pepper on the image. For these reasons, is that bipolar impulse noise named salt pepper noise, too. Noise strokes can be positive or negative. Scaling is part of the digitalization process. As Impulse contamination is usually Greater than the Strength signal of the image, so the impulse noise points Usually find the highest value (black and white) after digitalization. Therefore, we assume the value of A and b are Given Usually by the allowable minimum and maximum values in the saturated images. Thus, the negative impulse will see as black spots (pepper) and positive impulses as white points (salt) in the images. One of the best filters to eliminate this type of noise is the median filter or Gaussian filter. Although these filters can eliminate the noise of the image, it usually causes that darkened or smoothed image or its edges and does not need to do change and change all pixels, reducing noise will be done because moving non-noise-pixel values with neighboring pixels and generally results in a decrease quality image at non-noise points Figure 2. 4 intensity scaling. The problem of MSE is avoided by PSNR, which is a measure for comparing the denoising results of the same image. PSNR is defined based on the logarithmic decibel scale. 2.8 SSIM (Structural Similarity Index) The SSIM is a new metric than other metrics like PSNR and MSE and calculates the Structural Similarity (SSIM) index between two images. This parameter is according to the human visual system and improves the traditional metrics. SSIM is present with Equation (8), Where x and y are the two images [24]. SSIM ( x, y ) 2 x 2 x y c1 2 xy c2 y c1 x y c2 2 2 In equation (8), x is the average of 2 (8) x , y is the average of y , xy is the covariance of x and y , x2 is the variance of x , x2 is the variance of y , x2 c1 (k1L)2 , c2 (k2 L)2 two variables to prover the division with the weak denominator L is the dynamic range of the pixel value k1 =0.01 and k2=0.03 3. Non-Local Means (NLM) The nonlocal mean filter is an algorithm for image noise removal in image processing also is used as a filter in the spatial domain. The benefit of this Filter is Gray-Scale image redundancy and structural redundancy by giving the weighted average to the pixel values to predict the current pixel value. The quantity of each pixel is measure by the weighted Euclidean distance between the sub-blocks, which ensures that Were assigned more weight to the pixels with similar structure [25]. Fig. (2). Salt and pepper noise. 2.7 Peak signal-to-noise ratio Suppose a gray scale image z ( z ( x )) x defined on the The Peak Signal-to-Noise Ratio (PSNR) is a Good metric in image processing Which It Defined as the ratio between the maximum possible power of a signal and the power of corrupting noise and use in the measuring of quality image. After acquiring the reconstructed image and image processing process, we should get PSNR metrics. With PSNR, we compare the quality of the original image and the reconstructed image. PSNR does define by equations 6,7 [22, 23]: domain R . Z (x) is the noise intensity perceived at x position. As displayed in Figure 3, the estimation intensity of the NL( z)( x) noise remove at the x position is calculated as 1 2 MSE M , N I1 (m.n) I 2 (m, n) (6) M *N of the local neighborhood blocks concentrate on the pixels x and y. Also, the number of weights that content the conditions 2552 MSE 10 PSNR 10log 2 the average weighted of the intensity pixels image [26]: NL( z )( x) w( x, y )z ( y ) the weight value w( x, y) depended resembles the intensity is w( x, y ) (7) To estimate the efficiency of any image denoising method, commonly used matrixes i.e. are Mean-Squared Error and Peak Signal-to-Noise Ratio. Levels of Gray are 256; M, N is the number of the rows and columns of the image, I1 is the original image I2 is the reconstructed image. To decreasing MSE, the PSNR will be high. But the problem with meansquared error is its dependency on the scaling of image (9) y 0,1 and y w( x, y) 1 . Title of the Article Journal Name, 2019, Vol. 0, No. 0 Fig. (3). Non-local mean filter Weight values w( x, y) Generally Depend on the similarity 5 Figure 4 shows a few samples of the signal corresponding to different rows or columns of an image. with this idea, wavelet transform can be applied to each row or column of the image separately. in fact, the procedure of implementation of the two - dimensional wavelet transform is the same procedure. in other words, for use the two dimensional wavelet transform in the image, at first the one dimensional wavelet transform is used to the rows and then the columns are downsampled with rate, 2 to keep only the specimens located in even places. in this test, the one dimensional wavelet transform is used to the columns again and the rows are finally downsampled with a rate of 2. in this way, 4 different sub-bands are obtained as the wavelet coefficients of the image. of the patch system and the size of the gap between the two patches. w( x, y) is the weight allocated to the intensity value N ( y) for return x pixels. The weight function w( x, y) defined in relation (2): v( N ) v( N ) x d d y d d 1 w( x, y) exp 2 z ( x) h 2 (10) h acts as a parameter that controls the degree of smoothing. v( N x ) specifies the patch vector in the which focused on the pixel x. that guarantees the y d d size image, z ( x) is a normalization stable w( x, y) 1 value and defined in relation (3). v( N ) v( N ) x d d y d d z ( x) y h2 2 2a (11) The nonlocal mean filter (NLM) uses repetitive information image on this basis that the structural resemblance takes in on the noise pixel is random and thus can effectively eliminate the noise in the image using weighted mean [27, 28]. This method looks like white noise, as less disordering in the denoised produce [29]. Result of simulation denoising medical image by Nonlocal means displayed in figure 7, 8. PSNR of the original image and the denoised image were calculated. Metrics SSIM, MSE, RMSE is calculated, too. 4. Wavelet Transform wavelet transform is used in mathematical transformations. in the field of processing domain and especially signal and image processing. according to the nature of the multi-resolution analysis, this transformation has opened its place and role in many processing applications and introduce sometimes as a powerful tool. in each two - dimensional signal which is generally referred to In each two-dimensional signal which, is referred to generally as the image a matrix of elements is present in rows and columns. With some accuracy can be seen that each row or, the column of an image can be imagined as a one dimensional signal whose amplitude values represent the brightness of the pixels in that column or row particularly. Fig. (4). One-dimensional signals obtained from multiple arbitrary rows and columns of a two-dimensional Signal (image) Continuous wavelet transform (cwt) presented as an alternative method to short-time Fourier transform, and Its purpose is overcoming the resolution problems in short-time Fourier transform. In wavelet analysis, similar to the Fouriershort-time Fourier transform, the signal (image or sound) multiplied into a function (wavelet) that plays the same Function as the window. Similar to the previous one, the wavelet transform is also performed separately on different signal epochs. Accordingly, the continuous wavelet transforms defined as: CWTx , s x , s 1 s t (12) x(t )* dt s Where τ and s are the transmission and scaling parameters respectively. The concept of transfer is the same as the concept of time transfer in the Fourier-Short-Time Convert, which shows the rate of window shift and includes the time information conversion. But unlike short-time wavelet transforms, we do not have directly a frequency parameter in wavelet transforms. Instead, use the scale parameter that is inversely the frequency, or, s = 1 / f. Wavelet transform (WT) have multiple scales and some methods such as thresholding and statistical modeling use for noise removal that is very effective. 5. Convolutional Neural Network The Deep Learning concept came out of the when new methods and strategies were introduced to resolve the Title of the Article previous problems, and speed up particular when it came to Alex krizhevsky and Jeff Hinton in 2012 could with a deep convolutional network, the ImageNet competed was win successfully and reach the first rank. Sample of Deep learning structure is shown in figure 5. Journal Name, 2019, Vol. 0, No. 0 6 most existing techniques for image denoising try to estimate the original and clean image x from a noisy image z in (13). in, [35] defined image denoising with residual learning of deep convolutional neural network (DnCNN) [36-38]. Sample of medical image denoising model is displayed in Figure 6. Fig. (5). Deep learning structure In learning our hierarchical feature, we extract non-linear features of several layers, and then we pass them into a classifier, which combines all of these features so that it can perform a prediction. The more this layer hierarchy is more (deeper) in result it will get in more nonlinear features. Therefore, we are also interested in using more layers in deep learning (in principle, this second part of deep learning follows the same principle and points to a hierarchy that helps you to learn the features). On the other hand, these complex features cannot be obtained directly from the input image. It can be indicating mathematically that the best features that can be obtained from an image using a layer (without a hierarchy) are just edges and masses (edges and blobs). The reason for this is that there is information that we can get from a nonlinear transform of the input image. To obtain or produce features that include more information, we cannot work directly on the input image, and we need to re-convert our early features (such as edges and masses) to more complex features that include more information is needed to distinguish between classes. This way of working we see in deep learning essentially derives the idea of the human brain and how the visual cortext works in the human brain. In the human brain, the primary hierarchy neurons in the visual cortex also have the information they receive sensitive to the edges and masses, and then their output goes on in a subsequent hierarchy, until the neurons into more complex structures such as facial, Showing the sensitivities. Remember that the learning of the hierarchical feature exists before the Development of the deep learning field, but the Architecture of old-time suffered from main problems like the Fading of the gradients. Gradient fading is essentially a Problem in Gradients, so small that they can’t create a learning signal for deep layers, and therefore these architectures are compared to inferior learning algorithms such as (support vector machines). They were doing many badly. Deep-Learning is a subset of Machine learning and extracts information based on multi-level Learning. Performance of deep-Learning is greats and effective with the development of deep Learning [30-32], Denoising autoencoder [33] and convolutional denoising autoencoders (CNN DAE) [34] investigated in image denoising. All the techniques of image denoising have a formulation, (13) Z X V The mixture of image x and some noise v, where constitute z the noisy image. If we consider this formulation, Fig. (6). A model for medical image denoising This model is summarized as follows: In this structure denoising method is done over a small training dataset, and the noisy image is estimated based on a deep feed-forward convolutional neural network model, and it is a pioneer, and the Main model in denoise image result [36-38]. The Result of the simulation denoising medical skin image by the convolutional neural network display in figure 13. PSNR of the Original and the denoised image display. SSIM, MSE, RMSE metrics calculated, too. 6. Results and Simulation The four filters: the adaptive Wiener filter, Wiener filter, the median filter, and the adaptive median filter is simulated with (MATLAB 2017b) and tested for two noises: Gaussian Noise and Salt & Pepper Noise on the skin image. The next two sections explain the results. Fig. (7). Gaussian Noise, Noise Density =1%- PSNR=28.11, SSIM=0.6, MSE=100.41, RMSE=10.02 Send Orders for Reprints to reprints@benthamscience.ae Journal Name, Year, Volume 7 Fig. (8). Ssim Index Map 6.1. Qualitative Analysis Figures 9 (A)–(C) and 12 (A)–(B) show skin image and noise densities (1%, 10%, and 50%). The noisy image is reforming using Wiener, Median, and Adaptive Median filters. The result of the Adaptive Wiener filter specifies. It is not Good filter skin image quality for Gaussian noise and, Good filter skin image for Salt and Pepper. The performance of the Median filter is Good for Gaussian noise that confirms the result of this filter. Per results, the Adaptive Median is a good filter for salt and Pepper noise compared to the Wiener filter and Median and Adaptive Wiener filter. But, this filter not has a good result for Gaussian noise. The PSNR of the simulated image does calculate and shown in figures 9–12. A XXX-XXX/15 $58.00+.00 © 2015 Bentham Science Publishers Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 8 B C Fig. (9). Median Filter, Gaussian Noise. (A) Noise Density=50%- PSNR=12.0668, (B) Noise Density =10%- PSNR= 17.6599, (C) Noise Density =1%- PSNR= 25.5733 A B C Fig. (10). Adaptive Median Filter, salt & pepper Noise. (A) Noise Density =50%- PSNR=14.9867, (B) Noise Density =10%PSNR= 32.8514, (C) Noise Density =1%- PSNR= 33.2644 Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 9 A B C Fig. (11). Adaptive Wiener Filter, Gaussian Noise. (A) Noise Density =50%- PSNR=14.4527, (B) Noise Density =10%PSNR= 18.5817, (C) Noise Density =1%- PSNR= 26.7983 A B Fig. (12). Wiener Filter, Gaussian Noise. (A) Noise Density =0.1%- PSNR= 23.94, (B) Noise Density =1%- PSNR= 11.19 Figure 10 Display the Result of the simulation Denoising by a convolutional neural network (CNN) for medical skin images. PSNR and SSIM, MSE, RMSE for image calculate. Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 10 Fig. (13). Gaussian Noise. (A) Noise Density =1%- PSNR=30.11, SSIM=0.6, MSE=25.65, RMSE=5.06 6.2. Quantitative Analysis The result of the simulation of this paper show in figures 715, PSNR image each filter calculates and specify the noise Density. Image denoising methods discuss in Table 1. In Table 2 and Table 3 are different data set. Several papers review skin denoising and machine learning techniques. Some papers chose to use a large dataset. The running time of Detection or Classification Depends on the Efficiency of the Systems and the number of Input images and Data set. We studied some papers such as Medical image forgery detection for smart healthcare [39] and ROI-based fragile watermarking for medical image tamper detection [40] and Enhancing the security of exchanging and storing DICOM medical images on the cloud [41] and [42] and Medical image resolution enhancement for healthcare using nonlocal selfsimilarity and low-rank prior [43]. Table 1. Review of medical denoising approaches. Title, Author, Publication, Year Title:” A Review of Denoising Medical Images Using Machine Learning Approaches” [44] Authors:” Prabhpreet Kaur, Gurvinder Singh and Parminder Kaur” Publication: “Bentham Science (2018)” Title: “A WAVELET APPROACH FOR MEDICAL IMAGE DENOISING” [45] Authors:” Gagandeep Kaur, Romika Choudhary, Ashish Vats” Publication: “IJARCS (2017)” Classes Medical Images denoising Title: “Performance evaluation of wavelet, ridgelet,curvelet and contourlet transforms based techniques for digital image denoising” [46] Authors: “Vipin Milind Kamble, Pallavi Parlewar, Avinash G. Keskar, Kishor M. Bhurchandi” Publication: “Springer (2015)” Image denoising Image denoising Tools/Techniques wavelets, curvelets, Advantages the machine learning performance and accuracy is better than the conventional image denoising techniques, remove gaussian noise Daubechies, Haar, Symlet, Thresholding in wavelet transform X’let transform Provide effective denoising Deep learning, Convolutional neural networks survey Medical imaging Title:”A survey on deep learning in medical image analysis” [47] Authors:” Geert Litjens ∗, Thijs Kooi , Babak Ehteshami Bejnordi , Arnaud Arindra Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 Adiyoso Setio , Francesco Ciompi, Mohsen Ghafoorian, JeroenA.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez” Publication: “Elsevier (2017)” Title:”MRI Medical Image Denoising by Fundamental Filters” [12] Authors:” Hanafy M. Ali” Publication: “INTECH (2018)” Medical image denoising using convolutional denoising autoencoders [34] Authors:” Lovedeep Gondara” Publication: “IEEE 16th Conference (2016)” Title:” On the Accuracy of Denoising Algorithms in Medical Imaging: A Case Study” [48] Authors:” Fabrizio Russo” Publication: “IEEE (2018)” MRI Medical Image Denoising non-linear filter, median filter, adaptive filter and, adaptive median filter Provide effective denoising Medical image denoising Conventional autoencoder increase denoising performance Medical Image Denoising PSBR, Anisotropic diffusion-based filters preserve the image details during noise removal Table 2. Explored medical denoising and machine learning techniques. Title, Author, Publication Dataset Features Title:”Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis” [49] Authors:” Oludayo O. Olugbara, Tunmike B. Taiwo, and Delene Heukelman” Publication: “Hindawi (2018)” Medical image denoising using convolutional neural network: a residual learning approach [50] Author: “Worku Jifara, Feng Jiang, Seungmin Rho, Maowei Cheng,Shaohui Liu” “Springer(2017)” Title: “3D Medical Images Denoising” [51] Author: “Feriel Romdhane, Faouzi Benzarti and Hamid Amiri” Publication: “IEEE IPAS’14 Dermoscopic images mean of coefficient of variations (MCV), AVER, STDR, Chest radiography, Mammograms image PSNR, SSIM, Variance σ Tools/Techniques Used perceptual color difference saliency (PCDS) Classification Approach Segmentation of Melanoma Skin CNN DAE, DnCNN Medical image, Image denoising, , Residual learning BM3D 3D medical images RMSE, PSNR, MRI image RMSE, PSNR, SSIM, Thresholding Mean, variance, skewness and kurtosis Classifiers such as SVM, kNN, and NB SSIM, SNR combine NL-mean approach with the anisotropic diffusion tensor Image Denoising Using NL-mean approach with the anisotropic diffusion tensor WAVELETS, Image Denoising Using Wavelet INTERNATIONAL IMAGE PROCESSING APPLICATIONS AND SYSTEMS CONFERENCE(2014)” Title: “Different Denoising Techniques for Medical Images in Wavelet Domain” [24] Author: “Smriti Bhatnagar R.C.Jain,” Publication: “IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICSC) (2013)” Title: “A Novel Approach for Classifying Medical Images Using Data Mining Techniques” [52] Author: “J. Alamelu Mangai, Jagadish Nayak and V. Santhosh Kumar” Publication: “IJCSEE (2013)” Title: “ Image Coding Using Wavelet Transform” [53] Author: “Marc Antonini, Michel Barlaud,Pierre Mathieu, and Ingrid Daubechies” Publication: “IEEE TRANSACTIONS ON IMAGE PROCESSING (1992)” Title: “A GA-based Window Selection Methodology to Enhance Window-based Multi-wavelet transformation and thresholding aided CT image denoising technique” [54] Author: “Prof. Syed Amjad Ali, Dr. Srinivasan Vathsal, Dr. K. Lal Kishore” Publication: “International Journal of Retinal fundus images of size 576x720 pixels. “The intensity of each pixel is coded on 256 grey levels (8 bpp), 256 by 256 black and white images.” Industrial CT volume data sets Entropy, PSNR Number of window selected, Gene length, Mutation Rate, PSNR values Wavelet Coefficients, Vector Quantization Window based Multiwavelet transformation and thresholding, Genetic algorithm Machine Learning classifiers Machine Learning Window Based Multi-wavelet classification 11 Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 Computer Science and Information Security (2010)” Title: “Adaptive image denoising using cuckoo algorithm” [55] Author: “Memoona Malik, Faraz Ahsan, Sajjad Mohsin” Publication: “Springer (2014)” Title: “Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm” [56] Author: “Danilo Cesar Pereira, Rodrigo Pereira ramos, Marcelo Zanchetta do Nascimento” Publication: “Elsevier (2014)” Title: “Mixed Curvelet and Wavelet Transforms for Speckle Noise Reduction in Ultrasonic B-Mode Images” [57] Author: “A.A. Mahmouda, S. El Rabaiea, T.E. Tahaa, O. Zahrana, F.E. Abd ElSamiea and W. AlNauimy” Publication: “Information Science and computting (2015)” Title: “Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm” [58] Author: “Yali Liu” Publication: “International Journal of Signal Processing (2015)” Standard512{512 images (‘Lena’, ‘Pirate’, ‘Mandrill’) IQI, VIF, both IQI and PSNR or both IQI and VIF Database taken from Digital Database for Screening Mammography (DDSM) “Distribution separation measure, target to background contrast enhancement measurement based on entropy, target to target background contrast enhancement measurement based on standard deviation, combined enhancement measure” Six ultrasonic Bmode images (Liver, Kidney, Fetus, Thyroid, Breast and Gall PSNR value, Coefficient of Correlation (CoC) Images of Lena and Saturn Planet Hard Threshold Function, Soft Threshold function Cuckoo search algorithm Wavelet transform, genetic algorithm Wavelet and curvelet transform Wavelet Transform, Genetic Algorithm Comparisson of Cuckoo Search With existing Artificail intelligence techniques Artifact removal algorithm fusing gray level enhancement method and image denoising and using wavelet transform and wiener filter Wavelet transform handles homogeneous areas while curvelet transform handles areas with edges Genetic Algorithm Table 3. Machine learning methods. Title Author Publication Title: “A Comparative Study of Classification Algorithms in E-Health Enviroment” [59] Author: “ M.A. Hassan” Publication: “IEEE Conf 2016)” Title: “Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods” [60] Author: Shan, J., Alam, S.K., Garra,B., Zhang, Y. and Ahmed, T. Publication: Science Direct (2015) Title: “Machine Learning Approaches in Medical Image Analysis: From detection to diagnosis” [61] Author: “Bruijne M.” Publication: “Elsevier (2016)” Application & Dataset Techniques Parameters MEDICAL IMAGES (E-HEALTH ENVIRNMENT) DATASET: 600 INSTANCES FROM PUBLIC HOSPITAL SAUDI ARABIA Classification Algorithms (Bayes Net, Logistic, K Star, Stacking, JRIP, One R, PART, J48, LMT, RF) Precision, TP “True Positive”, Recall, FP “False Positive”, F-Measure, Time, ROC Area ANN, SVM, Decision Tree, Random Forest, Student’s t –test Shape, Orientation, Margin, Echo Pattern, Posterior Feature Machine Learning Diagnosis Methods, Imaging Protocols, Labels Confounding FactorsAge, Gender, Curves Visual Performance CAD FOR BREAST ULRASOUND DATASET: 283 US IMAGES (133 BENIGN AND 150 MALIGNANT DETECTION OF DIABETIC RETINOPATHY, BRAIN MRI IMAGES ETC DATASET: 35,000 Strengths “ROC Area concludes Random Forest has highest Rate”. “Bayes Net, K star, Stacking, OneR, J48 take least time 0.01 followed by PART 0.08 sec, then Logistic with 5.4 Sec and LMT took 12.2 sec.” “Bayes Net is the best classifier for patient data set in terms of performance metrices with TP 0.987, FP 0.002, Precision Rate 0.988, Recall rate 0.987, F-measure 0.988,ROC 0.994,time 0.01 sec.” “Best ROC performance” “Better performance of clustered classifiers in a tumor classification task.” “Train strong Models on little data, Improve access on Data, Best make use of image structure, Properties in designing models” Limitations Decision making of classifiers is limited on huge dataset Hybridization of classifiers have been ignored Theoretical base is explained 12 Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 Title: “Hybrid Approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning” [62] Author: “H. Ravishankar, S. Prabhu, V. Vaidya, N. Singhal” Publication: “IEEE Conf (2016)” Title: “A Novel Approach for Classifying Medical Images using Data Mining Techniques” [52] Author: “Mangai J. A.” Publication: “IJCSEE (2013)” IMAGES OF DIABETIC RETINOPATHY Fundus Images Dataset: “32 very severe images and 61 normal Fundus images ” Fundus Images Dataset: “32 very severe images and 61 normal Fundus images ” “k nearest neighbor (kNN), Support Vector Machine(SVM)a nd Naïve Bayes(NB)” “k nearest neighbor (kNN), Support Vector Machine(SVM)a nd Naïve Bayes(NB)” Median filters performance PSNR shown in Table 4,5 for different variances Table 4. Median filter/ Gaussian Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 29.75 28.9 26.33 14.78 SSIM 0.78 0.76 0.71 0.17 MSE 16.47 17.4 18.8 34.15 RMSE 4.05 4.17 4.33 5.84 Table 5. Median filter/ salt & pepper Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 25.58 17.65 15.15 12.05 SSIM 0.45 0.12 0.07 0.03 MSE 51.57 96.95 106.19 113.95 RMSE 7.18 9.84 10.3 10.67 Adaptive Median filters performance PSNR shown in Table 6,7 for different variances Table 6. Adaptive Median filter/ Gaussian Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 33.27 32.82 29.08 15.01 SSIM 0.9 0.90 0.8 0.2 MSE 7.3 7.2 8.1 24.83 RMSE 2.7 2.6 2.8 4.9 Table 7. Adaptive Median filter/ salt & pepper Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 22.8 14.6 12.6 10.7 SSIM 0.3 0.07 0.04 0.2 MSE 71.24 10.5 117.8 110.8 RMSE 8.4 10.5 10.8 10.9 Adaptive wiener filters performance PSNR shown in Table 8,9 for different variances Table 8. Adaptive wiener filter/ Gaussian Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 27.05 18.72 17.24 15.87 SSIM 0.66 0.23 0.16 0.10 MSE 43.43 90.33 98.62 104.2 RMSE 6.5 9.5 9.9 10.2 Discretization Method: Receiver Operating Characteristics ( ROC) in terms of accuracy and area Minimal Description Length (MDL) Discretization Method: Receiver Operating Characteristics ( ROC) in terms of accuracy and area Minimal Description Length (MDL) AUC outperform “NB classification performance outstanding” “NB is 0.94 as compare to kNN and SVM” AUC outperform “NB classification performance outstanding” “NB is 0.94 as compare to kNN and SVM” 13 Data set is limited to only fungus retinal images Data set is limited to only fungus retinal images Table 9. Adaptive wiener filter/ salt & pepper Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 25.48 18.82 17.3 14.3 SSIM 0.64 0.22 0.16 0.08 MSE 15.58 53.9 110.52 161.19 RMSE 3.9 7.3 10.5 12.69 wiener filters performance PSNR shown in Table 10,11 for different variances Table 10. wiener filter/ Gaussian Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 PSNR 11.18 4.14 3.52 3.1 SSIM 0.50 0.13 0.07 0.02 MSE 0.07 0.38 0.44 0.48 RMSE 0.27 0.62 0.66 0.69 Table 11. wiener filter/ salt & pepper Noise performance PSNR Sigma 0.01 0.1 0.2 0.5 0.001 PSNR 11.16 4.17 3.54 3.14 24.6 SSIM 0.55 0.14 0.07 0.02 0.69 MSE 0.07 0.38 0.44 0.48 0.0035 RMSE 0.27 0.61 0.66 0.69 0.05 Stroke Lesion Segmentation Current Bioinformatics, 2017, Vol. 0, No.0 14 Figure 15 and Table 13. The Adaptive Median filter show higher PSNR than other filters, Weiner filter. In this test, the Median filter is allowed for removing two applied noises. Table 12. PSNR of different filtering methods (Gaussian noise). σ Gaussian Noise Median filter Adaptive Median filter Wiener filter Adaptive Wiener filter 0.01 0.1 0.2 0.5 25.58 22.8 17.65 14.6 15.15 12.6 12.05 10.7 11.18 27.05 4.14 18.72 3.52 17.24 3.1 15.87 To comparing Four Filters, The Adaptive Wiener filter has the best performance for skin image, as shown in Table 12, and has high PSNR. Adaptive Wiener filter gave a Good performance to remove Gaussian noise. Fig. (14). PSNR of different filtering methods (Gaussian noise). Table 12. shows the result of tested experiments for filters and shows PSNR values of each filter (Wiener filter and Adaptive Wiener filter, Median filter, and Adaptive Median filter). Used Noise in this test is Gaussian Noise. Its density in the skin image changing from a 1,10,50%. In comparison, these Four Filters the Adaptive Wiener filter has the best performance for Gaussian Noise, and its results were displayed in Figure 14. The adaptive Wiener filter has a higher PSNR than other filters shown in Table 12. The performance of the Wiener Filter is not good at removing Gaussian noise. Also, Image catch blurring that shown in Figure 14 and Table 12. Table 13. PSNR of different filtering methods (Salt & Pepper Noise). σ Salt & Pepper Noise Median filter Adaptive Median filter Wiener filter Adaptive Wiener filter 0.01 0.1 0.2 0.5 29.75 33.27 28.9 32.82 26.33 29.08 14.78 15.01 11.16 25.48 4.17 18.82 3.54 17.3 3.14 14.3 To comparing the Four Filters for skin image, The Adaptive Median filter has a Good result, as displayed in Table 13, and has a high PSNR. The adaptive Median filter gave a Good performance to remove Gaussian noise. The wiener filter gave a worse performance in remove Gaussian and salt, and pepper and has low PSNR. CONCLUSION This research studied several Methods for medical image denoising by using Image Processing and Deep Learning and Machine Vision. The paper presented a review of different Researches based on image denoising analysis of dermatological images. First of all, the different steps needed for medical image denoising are introduce. Afterward, each step was explained, studied, and Different methods introduce. Performance of the methods Shows in the simulation section. PSNR values CNN methods is higher and better than all filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). Fig. (15). PSNR of different filtering methods (Salt & Pepper Noise). Table 13 shows the PSNR of each filter (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). Each filter used the Salt and Pepper Noise, and Its density is selecting for medical skin image changing from 1– 50%. According to the Result in this paper, The Adaptive Median filter has the best results. Also, its results show in ETHICS APPROVAL AND CONSENT TO PARTICIPATE Not applicable HUMAN AND ANIMAL RIGHTS Not applicable Stroke Lesion Segmentation CONSENT FOR PUBLICATION Not applicable AVAILABILITY OF DATA AND MATERIAL Not applicable. FUNDING None CONFLICTS OF INTEREST None. ACKNOWLEDGEMENTS None. 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