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Denoising Medical Images Using Machine Learning, Deep Learning Approaches:
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Article in Current Medical Imaging Reviews · November 2020
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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
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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
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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
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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 .
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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
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Journal Name, Year, Volume
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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
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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
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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.
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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
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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
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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
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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
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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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Revised: ---
Accepted: ---