2021 2nd International Conference for Emerging Technology (INCET)
Belgaum, India. May 21-23, 2021
2021 2nd International Conference for Emerging Technology (INCET) | 978-1-7281-7029-9/20/$31.00 ©2021 IEEE | DOI: 10.1109/INCET51464.2021.9456201
An Empirical Study of Dehazing Techniques for
Chest X-Ray in Early Detection of Pneumonia
Deepa Abin
Sudeep D.Thepade
Computer Engineering Department,
Computer Engineering Department,
Pimpri Chinchwad College of Engineering, Pimpri Chinchwad college of Engineering,
SPPU, Maharashtra, India
SPPU, Maharashtra, India
deepaabin@mail.com
sudeepthepade@gmail.com
Sanskruti Dhore
Computer Engineering Department,
Pimpri Chinchwad College of Engineering,
SPPU, Maharashtra, India
sanskrutidhore@rediffmail.com
image eliminates unwanted visual effects and is often
considered as an image enhancement technique [1,2].
Abstract— Pneumonia is one of the major infectious disease
which affects most of the population. In day-to-day life, the use
of technologies is increasing rapidly. Imaging plays a major
role in the detection of many diseases. Chest-X-Ray images in
medical imaging play the main role in the diagnosis of
pneumonia patients. But chest X-Ray images are not much
clear and this creates a problem in the detection of pneumonia.
Presents of haze can destroy the quality of these images. If the
quality of an image is low, doctors may not able to predict
early whether the Pneumonia present or not. This is the main
reason because of which the mortality rate increases. Thus
image dehazing techniques are used to improvise the quality.
The main aim of this paper is to perform experimentation of
dehazing techniques on X-Ray images and analyze the results
which improve the quality of images.
In this paper three image dehazing methods are applied
such as Histogram Equalization, Contrast Limited Adaptive
Histogram Equalization, and Dark-channel-prior. After
applying these techniques quality of the resulting images is
calculated using performance measures like Entropy and
NIQE.
The further part of this paper is organized as follows,
section II discusses related research work for dehazing
techniques. Section III presents various methods for
dehazing techniques and their output. Section IV shows
experimentation & results using performance metrics.
Finally, section V concludes the paper.
Keywords— Image hazing, Dehazing, Pneumonia, Chest XRay, Performance metrics.
I.
II.
INTRODUCTION
Wang Rui, Wang Guoyu [1], presents a Medical X-Ray
Image Enhancement Method. In this paper, the dark-channelprior method is applied to medical X-Ray images which
shows that the method work to increase the image
dissimilarity, highlights the details effectively. Quality of
image is measured using entropy value [1].
Pneumonia is a lung infection disease, which is caused by
bacteria, viruses, and fungi. When infection causes air sacs
into the lungs, pneumonia happens. This infection may cause
in either one or in both the lungs. Lungs are filled with fluid
or pus. This makes the breathing problem.
The process of generating a vision-based representation
of the internal structure of the human body for medical
diagnosis is nothing but medical imaging. Medical imaging
always probes to disclose internal structures hidden by the
skin and bones, also detects the diseases. Nowadays various
imaging technologies like X-Ray radiography, fluoroscopy,
magnetic
resonance
imaging
(MRI),
medical
ultrasonography, endoscopy, etc. are available [1].
In Shibin Wu's [2] paper, multiscale transform and
CLAHE based enhancement methods present for enhancing
the X-ray images. Laplacian pyramid decomposes the input
image and extracts the characteristics of a multiscale image.
Then CLAHE is applied to X-Ray images for enhancing the
contrast of images. The image is reconstructed using an
inverse Laplacian pyramid and the image that obtains is an
enhanced image. The performance of this proposed
algorithm is then evaluated by contrast evaluation criteria for
image and information entropy [2].
The X-Ray image is one of the imaging techniques which
is used to examine luggage for the presence of weapons or
bombs, used to detect structural cracks in metals, commonly
used in the field of medicine to detect fractures, pneumonia,
to reveal the architecture of bones, used in dental imaging.
Himanshu Singh and Vivek Singh [3] compares various
histogram equalization techniques for medical image
enhancement. This paper compares and evaluates the results
of medical image techniques like Histogram Equalization,
Adaptive Histogram Equalization, Brightness- PreservingBi-Histogram-Equalization, and CLAHE.
X-ray is one of the best tool for pneumonia detection.
Even X-Rays are more preferable over the CT scan images
because it takes more time than X-Ray images. The
traditional X-Ray images were of poor quality and once it
produced, then their quality cannot be improved further and
these may cause problems to store, manage, and transmit [1].
The quality of images can be improved using image
processing techniques. Due to the complex human body
structure and tissue, as well as the lack of imaging equipment
and environment and other factors, X-Ray image quality is
poor. Dehazing improves problems of various computer
vision and image processing-based applications as it
diminishes the scene’s visibility. Removing haze from an
978-1-7281-7029-9/21/$31.00 ©2021 IEEE
LITERATURE SURVEY
In the review paper of Manpreet Kaur Saggu, Satbir Singh
[2015], they compared various dehazing techniques for
image processing. The main objective of this paper is to
explore different previous haze removal techniques used for
image processing applications. Dark-channel-prior for
removal, joint trilateral filter, CLAHE, MIX-CLAHE are
used in this paper [4].
The review paper [5] shows various methods to take out
fog from images captured in real-world weather conditions to
1
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construct fast and improve the quality image. These
techniques improve the color and contrast of the screen.
These methods are used for outdoor investigation, to detect
the object, to track segmentation, etc.
Sandeep Kaur, Pranav Kumar [2017], discusses various
techniques of image enhancement. This paper shows the
comparison between methods like Histogram-Equalization,
Brightness-Preserving-Bi-Histogram-Equalization, Dualistic
sub-image Histogram Equalization, Minimum-MeanBrightness-Error BI-Histogram Equalization, RecursiveMean-Separate Histogram Equalization, Mean-BrightnessPreserving Histogram Equalization [6].
Fig. 1. Original Image
The review paper presents a study of different haze
removal techniques. This review paper explores the
shortcomings of the earlier presented techniques [5].
Sandeep Kaur, Maninder Kaur proposed a real-time haze
removal system using histogram processing. They applied
dark-channel-prior and Haar Wavelet on each color
component image to get sub-bands of image and then
histogram equalization is applied on each LL sub-band.
Finally, a haze-free image is generated using an inverted
haze-free image into its color component. They used
Entropy, PSNR, and Standard Deviation as performance
metrics [13].
Shebastian Salazar-Colores, Juan-Manuel RamosArreguin, Jesus Carlos Pedraza-Ortega, J RodriguezResendiz, proposed an efficient single image dehazing
technique by modifying dark-channel-prior. The proposed
method gives better results in terms of both efficiency and
restoration quality. This method is suitable for highresolution images and real-time video processing [14].
Fig. 2. Histogram Equalization
B. Contrast Limited Adaptive Histogram Equalization
Contrast Limited Adaptive Histogram Equalization is
also called CLAHE. This method is used for the
enhancement of low-contrast images. To avoid noise
amplification in the adaptive histogram equalization CLAHE
method is used. This algorithm initially divides the image
into m*n non-overlapping blocks. Then apply HE on subparts of images to increase the contrast of each subpart of
images. Hence CLAHE image is more clear [2, 4, 11].
Wencheng Wang, Xiaohui Yuan reviews the main
technique for image dehazing. By dividing no. of approaches
into three parts like image enhancement, fusion, and
restoration. They analyzed the principles and characteristics
of these methods [10].
Sungmin Lee, Seokmin Yun, Ju-Hun Nam, Chee Sun
Won, Seung-Won Jung, analyze dark-channel-prior-based
image dehazing algorithms. This study helps to understand
the effectiveness of every step of the dehazing process [15].
The review paper [17], studies traditional dehazing
techniques that filter haze from images and improvised haze
free images.
III.
METHODS AND TECHNIQUES
The methods used for haze removal are discussed below:
A. Histogram Equalization
The image processing method which is used for contrast
adjustment using image histogram is nothing but Histogram
Equalization. HE provides better image quality of image
without any loss of information. Firstly, HE computes the
normalized histogram of the input image then CDF
(cumulative distribution function) of the normalized image is
calculated. Then it finds transformation and applies the
transformation of each pixel of the input image [1, 3].
Fig. 3. Original image and output of Contrast Limited Adaptive Histogram
Equalization
C. Dark-channel-prior
The dark-channel-prior is the image dehazing technique.
Initially, the Dark channel is constructed from the input
image. From the dark channel, atmospheric light and
transmission maps are acquired. Transmission maps are then
further purified and a dehaze image is reconstructed. This
technique is mainly used for the evaluation of atmospheric
2
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light on the haze-free image to get more results. These
methods mainly applied to the following:-
quality of the image and in the case of NIQE, If the NIQE
score is lower then the image quality is better.
The entropy formula is given below:-
i. Colorful items and surfaces
L
E(S) = - pi log2 pi
ii. Shadows of any object like a car, building, etc.
i=1
iii. Dark items or surfaces [1, 4].
where i=1,2,3,4…..L and frequency of occurrence of a pixel
at ith level is pi. NIQE stands for Naturalness Image Quality
Evaluator. A smaller score indicates better image quality.
The following figure shows images from datasets.
Fig. 4. Original Image & Output of Dark-channel-prior
The novelty of this paper is the experimentation of a
combination of dehazing techniques. Here we combine the
dehazing techniques like Histogram Equalization with Darkchannel-prior, CLAHE with dark-channel-prior, and vice
versa like dark-channel-prior with HE and dark-channelprior with CLAHE. Initially, we applied HE, CLAHE &
DCP methods on 100 chest X-Ray images. Then the output
of DCP passes to CLAHE to which we called it as CDCP.
The output of DCP is passed to HE to which we called
HDCP. Then the output of CLAHE is passed to the DCP
which we called as DCPC and the output of HE is passed to
DCP called DCPH. Entropy and NIQE are used to evaluate
performance.
IV.
Fig. 5. Normal images from Chest X-ray dataset
EXPERIMENTATION ENVIRONMENT
For experimentation purposes, the Chest X-Ray dataset is
taken from Kaggle. This dataset is mainly used for
pneumonia detection. Total 5,876 grayscales images are
available in this dataset which contains two types of classes,
i.e Normal and Pneumonia. This experimentation is done on
Matlab R2020b version. To measure the quality of the
image two different performance measures are used i.e.
Entropy and NIQE. A higher entropy value indicates better
TABLE I.
Fig. 6. Pneumonia Infected images from Chest X-ray dataset
V.
EXPERIMENTATION AND RESULTS
In this paper, we applied a combination of haze removal
techniques to improvize the quality of X-Ray images using
Entropy and NIQE.
The following are the results of entropy for 5 images.
ENTROPY CALCULATION FOR 5 IMAGES
Image
Original
HE
CLAHE
DCP
CDCP
DCPC
DCPH
HDCP
Person1
7.3383
6.6136
7.5596
7.3398
7.5324
7.1106
6.9318
6.4353
Person2
7.4693
6.4807
7.469
7.2779
7.431
6.8526
6.6991
6.3453
Person3
7.4357
6.4807
7.4188
7.3359
7.3469
7.1314
6.7905
6.2176
Person4
7.2996
6.6667
7.4571
7.2937
7.397
7.1197
6.9908
6.2798
Person5
7.3319
5.8354
7.4603
7.2499
7.4668
6.9404
6.3609
6.0212
Average
7.37496
6.41542
7.47296
7.29944
7.43482
7.03094
6.75462
6.25984
3
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The following are the results of NIQE for 5 images.
TABLE II.
NIQE CALCULATION FOR 5 IMAGES
Imag
e
Origi
nal
HE
CLA
HE
DCP
CDC
P
DCP
C
DCP
H
HD
CP
Pers
on1
2.832
2
2.81
81
2.814
1
3.79
26
3.58
85
5.42
87
4.30
11
3.84
63
Pers
on2
2.359
9
3.23
84
4.486
1
4.55
85
3.62
26
6.71
34
5.12
24
3.95
2
Pers
on3
2.940
1
3.94
83
6.203
1
4.27
07
3.85
25
5.51
09
5.04
48
4.31
03
Pers
on4
2.885
5
2.08
83
2.963
7
4.03
18
3.44
94
4.95
25
4.38
77
4.04
64
Pers
on5
2.222
6
2.83
99
3.456
7
3.88
41
3.22
31
5.89
66
5.66
69
4.25
55
Aver
age
2.648
06
2.98
66
3.984
74
4.10
754
3.54
722
5.70
042
4.90
458
4.08
21
Fig. 7. Average Entropy of
Combinations
Dehazing Techniques With Their
For 100 images average Entropy
TABLE III.
AVERAGE ENTROPY OF 100 IMAGES.
Method
Average
Original
7.32926
HE
6.410365
CLAHE
7.486665
DCP
7.290111
CDCP
7.347803
DCPC
7.157746
DCPH
6.876927
HDCP
6.254483
For 100 images average NIQE
TABLE IV.
AVERAGE NIQE OF 100 IMAGES
Method
Average
Original
3.044084
HE
2.701643
CLAHE
3.43981
DCP
4.479537
CDCP
3.491578
DCPC
4.553056
DCPH
5.088588
HDCP
4.20508
Fig. 8. Average Of NIQE of Dehazing Techniques With Their
Combinations
The above table shows, the entropy of the average of 5
images and NIQE of 5 images. In table 1, we can see
CLAHE and CDCP (i.e. CLAHE with DCP) give more
average value i.e. 7.47296 and 7.43482 respectively. In table
2, we can see the HE and CDCP(i.e. CLAHE with DCP)
method gives less NIQE score average value i.e. 2.9866 and
3.54722 respectively. In table 3, for 100 images CLAHE and
CDCP give 7.486665 and 7.347803 average respectively for
entropy. Whereas in table 4, HE and CLAHE give 2.701643
and 3.43981 average NIQE for 100 images. Fig 5 & Fig 6
shows a graphical representation of averages of Entropy and
NIQE applied on 100 chest X-Ray images respectively.
Graphical Representation of Entropy and NIQE is given
below
4
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VI.
[7]
CONCLUSION
Dehazing techniques remove the haze from images and
improve the quality of images. This experiment is performed
on 100 Chest X-Ray images. The paper presents a
combination of dehazing techniques which gives a more
clear image than dehazing techniques. This combination
technique is good for entropy than NIQE.
[8]
[9]
In the future, this proposed system can be applied to
other medical images like MRI images, CT scan images for
early detection of various diseases like lung cancer, fractures
in bones, and covid-19, etc. Further, it can be applied to
videos to improve video quality.
[10]
[11]
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