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International Journals of Advanced Research in Computer Science and Software Engineering ISSN: 2277-128X (Volume-7, Issue-6)
Research Article
June 2017
© www.ijarcsse.com, All Rights Reserved Page | 364
A Comparative Analysis on Histogram Equalization
Techniques for Medical Image Enhancement Himanshu Singh
*, Assistant Prof.Vivek Singh
Dept. of CSE/IT, ITM University Gwalior,
Madhya Pradesh, India
DOI: 10.23956/ijarcsse/V7I6/0269
Abstract— Image enhancement (IE) is such technique which makes the processing on image in order to increase its
effectiveness for computer to process. Enhancement is, used for improvement of the visual effects and the clarity of
image.Medical image is used as the important information and basis for the clinical diagnosis, while its quality
digressed because of the interferences caused by the human body structure, equipment’s, and environmental factors.
In this paper review, the enhancement of medical images using efficient algorithms based on HE techniques. Also
evaluate the result of medical image on technique to histogram equalization (HE), adaptive histogram equalization
(AHE), (BBHE) and contrast limited adaptive histogram equalization (CLAHE) to compare the pre-processing of
image.
Keywords— Image Enhancement; Histogram Equalization; Adaptive histogram equalization; Contrast limited
adaptive histogram equalization, PSNR; Entropy.
I. INTRODUCTION
In modern world due to the changing life style of human beings, everyday new types of diseases are emerging. It’s an
everyday challenge for doctors to effectively diagnose these diseases and provide remedy [1]. In today’s medical
diagnosis such a crucial part is played by Medical images. Medicinal pictures have a critical influence in today's
restorative finding. Medicinal imaging innovations, for example, Computerized Tomography (CT), and X-ray imaging
give clear and direct perspective of the neurotic ranges. They are fundamental apparatuses for distinguishing and
diagnosing different maladies. In any case, because of the constraint of imaging equipment, got therapeutic pictures
frequently introduce low determination or low difference. Medicinal picture upgrade intends to enhance restorative
picture difference or accentuation certain elements. It is important to build the recognition rate of different ailment and it
has been one of the key research zones of advanced picture preparing [2].
Several image enhancement methods can be used: One is the enhancement method based on histogram equalization
(HE: HE is a common method of IE, but which is unable to be effective enough in practical applications [3]. Thus the
researchers put forward a series of improved method, Pizer [4] proposed adaptive histogram equalization algorithm
(AHE), which use a window sliding on the image to calculate the local gray level histogram, mapping the center pixel of
the window. This algorithm makes full use of the field information, but needs to calculate the histogram distribution in
each window, which leads to the low efficiency and noise sensitivity.
Fig. 1. Medical Image Enhancement
Fig. 2. Enhancement Model
Input Image
Pre-Processing
Filter method
Post processing
Enhanced Image
Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
© www.ijarcsse.com, All Rights Reserved Page | 365
Zuiderveld [5] proposed contrast limited adaptive histogram equalization (CLAHE) algorithm on the basic of AHE
to avoid noise sensitivity. Many other enhancement methods are developed over the years such as brightness preserving
bi-histogram equalization (BBHE), bi- gray level grouping (GLG). This algorithm has improved performance but did not
remove the issues.
In the below section II present the related research work of medical image enhancement under histogram
techniques, section III present the different techniques of histogram equalization with some evaluate result. Where as in
Section IV shows applications areas of image enhancement and performance metrics on which enhancement result
computed.
II. LITERATURE REVIEW
Anita Thakur (2015) et al introduce that Human visual framework conciliates by a decent differentiation Images.
Picture upgrade strategies are best answer for enhancing the visual appearance of pictures to a human watcher. It likewise
saves the structure elements of the picture. Upgrade of the noisy picture information without losing any critical data is
exceptionally testing. There are numerous instabilities included while capturing picture and the execution of picture
improvement fluctuates with subject. It is entrenched that Fuzzy logic and fuzzy sets are great at taking care of numerous
vulnerabilities. [6].
M.Shakeri (2016)- Histogram division and playing out a different leveling for each sub-histogram is one of the
displayed arrangements. The partitioning technique and deciding the quantity of sub-histograms are the fundamental
issues specifically influencing the yield picture quality. In this review, a technique is presented for automatic
determination of the quantity of sub-histograms and thickness based histogram division prompting fitting yield with no
requirement for parameter setting. Every principle pinnacle is in a different area. Image Contrast is expanded with no loss
of picture details through deciding the quantity of sub-histograms in view of the quantity of fundamental pinnacles.[7]
He Wen (2016) presented a picture improvement calculation which in light of wavelet domain homomorphic filtering
and CLAHE. The picture is divided by DWT; the picture is disintegrated into low-frequency and high-frequency
coefficients of first layer of wavelet space. At that point the low frequency coefficients are prepared by an enhanced
homomorphic channel, and after that direct increased [8].
Se Eun Kim (2016) presents an entropy-based IE technique in the wavelet space. This strategy isused in the HIS
shading space. The low-frequency coefficients in the wavelet area are altered by the worldwide histogram-based
approach. The high-frequency coefficients are scaled by amplifying the entropy of the complexity characterized in the
wavelet area [9].
Cheolkon Jung (2016)- In this work, it proposed a powerful difference upgrade technique in light of dual tree complex
wavelet transform (DT-CWT) to work on an extensive variety of symbolism without IE. In the terms of improvement, it
utilized the nonlinear reaction of the human eye to the luminance to outline a logarithmic capacity for worldwide shine
advancement. Additionally, the nearby difference is upgraded by CLAHE in low-pass sub groups, which makes the
structure of picture clearer [10].
Jing-Wein Wang (2016) to manage these troubles, an illumination compensation technique, versatile SVD in the two-
dimensional discrete Fourier domain (ASVDF) and a productive brilliance indicator for lighting detection, for face
picture improvement are proposed in this method. The outcomes for the CMU-PIE, Color FERET, and FEI confront
databases demonstrate that the technique extensively enhances the nature of face pictures, even sidelong lighting,
accordingly enhancing the exactness of face recognition generously [11].
Qiong Song (2016) - They exhibited another way to deal with show HDR IR pictures with IE. In the first place, the
local edge-preservingfilter (LEPF) is used to isolate the picture into a base layer and detail layer(s). After the separating
technique, it utilized a versatile Gamma change to alter the gray dissemination of the base layer, and extend the detail
layer in light of a human visual impact guideline. At that point, we recombine the detail layer and base layer to get the
improve output [12].
G.N .Sagar et al. [2012], proposed IE technique with filtering techniques which shows the enhancement in the contrast
of x-ray images which were manipulated because of the noise and blurring. They use different filtering techniques to
remove noise such as median filter and to remove the high frequency details,mean filter is used. They used MSE and
PSNR to measure the performance of the proposed technique. Not only removing noise but it also has the capability to
increase X-ray image quality. But it has a lot of improvement to eliminate the noise in the X-ray images completely [13].
Sundaram et al. [2011], proposed the Histogram Modified CLAHE (HM CLAHE). It can manage the level of
complexity improvement, which frequently give the resultant picture as a strong contrast and brings the area points of
interest for understanding that is more important. It partners both histogram alterations as an optimization method and
CLAHE[14]. [2013], presented a neuro-fuzzy inference system to produce the enhanced image. HE is used for contrast
enhancement. [15].
Nercessian et al. [2013], proposed aIE strategy with the preferences: the coordination of both luminance and difference
masking phenomena, and a prompt method for altering overall brightness, and accomplishing dynamic range pressure IE
coordinate multi-scale improvement structure. Test comes about showed the capacity of the proposed calculation to
accomplish concurrent nearby and worldwide upgrades [16].
III. TECHNIQUES OF HISTOGRAM EQUALIZATION
A. Histogram Equalization
HE is the highest operated contrast enhancement technique due to the easiness comfort.HE makes density scattering
flatter and it expanses the gray level range to enhance the total contrast of the given image. Transformation of gray levels
Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
© www.ijarcsse.com, All Rights Reserved Page | 366
of the given image to its improved image level cumulative distribution function (CDF) has been utilized by this technique.
HE changes the mean brightness of image to middle of overall dynamic range, which is a drawback of HE because of
intensity saturation and annoying artifacts occurs [17].
(a) Input Image
(b) Histogram Image
Fig. 3. Histogram Equalization
B. Adaptive Histogram Equalization
AHE is different from the normal HE method because HE gives only one histogram but AHE method generates several
histograms corresponding to different area of the image and by using that it redistributes the intensity values of the image.
By using ADE method can improve the detection of spiculation on dense mammographic backgrounds.
Saeid et al. [18], proposed an AHE method for segmentation of blood vessels in color retinal images. There are two
methods of retinal vessel segmentation and these are first derivative of Gaussian matched filter and the other one is simple
Gaussian matched filter. AHE gives the improved results of contrast of image which enhance the quality of image of
retinal vessels used in identification of diseases like high blood pressure and diabetes. This method gives the raise of about
2 percent in accuracy as compared to previous methods with an accuracy of 0.9353. Analysis of this approach shows that
AHE method used in retinal vessel segmentation is based on threshold. Therefore, this approach is suitable for specific
type of images.
(a) Input Image
(b) Adaptive Histogram Image
Fig. 4. Adaptive Histogram Equalization
C. BI- Histogram Equalization
TheBrightness Preserving Bi-Histogram Equalization (BBHE) is a type of technique in which the image histogram is
divided into two parts. This is the type of method in which, partitions intensity is given by the mean brightness value of
input image, and this intensity is the average intensity of all pixels that combines to make the image given by input. After
that the BBHE autonomously makes the sub-picture square with as indicated by their applicable histograms in the
constraint that the correct set's samples ought to be mapped into the range from the base gray-level to the information
mean and the examples in the last set ought to be mapped into the range from the mean of the greatest gray level.
Therefore, the result of balanced sub-pictures is encompassed by each other around the info mean, which gives the
consequence of safeguarding mean brightness. [19].
(a) Input Image
(b) BBHE Image
Fig. 5. Brightness Preserving Bi-Histogram Equalization (BBHE)
D. BI- Gray Level Grouping (GLG)
The basic principle involved in this technique is as follows. Firstly, we arrange the histogram components in groups with
respect to a proper number of gray level bins according to their amplitudes for the reduction of the number of gray bins.
The main objective of this technique is to get a uniform histogram for a low contrast color image. Conventional
Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
© www.ijarcsse.com, All Rights Reserved Page | 367
histogram equalization results in under or over contrast image since it leaves too much empty space on the grayscale. The
drawback of GLG is that it is not computationally efficient compared to fuzzy-based methods. The quantitative analysis
represent that fuzzy-based methods are superior to GLG [19].
E. Brightness Preserving Dynamic Histogram Equalization (BPDHE)
In the advancement of HE and DHE, proposed new method BPDHE. In DHE, the histogram of input image is partitioned
into small parts which are called sub-histograms. This method is useful to providing brightness of an image and which
gives the new range to intensities [20]. The realistic image is provided by its look.
In this method, calculate the mean brightness between resultant image and input image which is shifted by
BPDHE, which preserves the mean brightness. And it equalizes the average intensity of input and output images. Many
different filters are used by BPDHE such as Gaussian filter,smoothing filter, etc. which makes the data smooth by
suppressing the noise of image in order to get clear image [21].
F. Contrast-Limited Adaptive Histogram Equalization (CLAHE)
CLAHE differs from ordinary AHE as it limits the contrast. This feature is also applicable to global HE, which gives rise
to CLAHE. It is mostly used in enhancement of low contrast retinal image. In case of CLAHE, a transformation function
derived from contrast limited procedure to each neighborhood pixel. [17].
(a) Input Image
(b) CLAHE Image
Fig. 6. Contrast-Limited Adaptive Histogram Equalization (CLAHE)
G. Adaptive DWT (ADWT) basedDynamic Stochastic Resonance( DSR)
A DWT is simply like other wavelet transform which uses wavelet coefficients. By using this technique the content
image of high frequency is produced. The DWT further divides the input image to form sub bands. These bands are
referred as High-High (HH), Low-High (LH),Low-Low (LL), and High-Low (HL). The image processing using DWT is
done by introducing high-frequency sub band images to the low-resolution original images which gives the output as the
improved image. The ADWT based DSR is a technique which is proposed to enhance the very dark images. This gives
much improve performance in the enhancement of very dark images. Also the computational complexity is very low in
this method. [22].
The difference between DSR and ADWT based DSR is that DSR uses external noise of an image and the ADWT
based DSR utilizes inside noise to give better execution of an input picture. It produces the yield without ringing, curios,
obstructing of the picture. This method of adding noise to the input picture is extremely helpful in the event of non-
straight frameworks. Because in SR component the flag can't have the capacity to achieve the limit an incentive by
utilizing lower noise, so it requires noise which enables the flag to achieve the edge esteem. In this manner ADWT based
DSR is reasonable for improvement of both the grayscale and colored picture.
IV. APPLICATIONS
IE is utilized to improve the image quality. IE are used in these applications namely, Aerial imaging, Satellite imaging,
Medical imaging and in remote detecting. IE strategies utilized as a part of numerous regions, for example, crime scene
investigation, Astrophotography and in Fingerprint coordinating, and so on. [19].
V. PERFORMANCE MEASURES
The performance parameters are histogram, entropy, SNR and PSNR.
A. Entropy
The average information content known as entropy is used for measure of image quality. Larger the value of entropy,
more the information content in the image.
𝐸𝑁𝑇 = − 𝐼 𝑙 𝑙𝑜𝑔𝑙(𝑙)𝐿−1𝑖=0 (1)
Where ENT (i) denotes entropy, I(l) is pdf of image having l intensity level and L denotes the number of gray levels.
B. Peak signal-to-noise ratio (PSNR)
For PSNR calculation first mean square error (MSE) is calculated as-
𝑀𝑆𝐸 = 𝑋 𝑖,𝑗 −𝑌(𝑖,𝐽 ) 2𝑁
𝑗=1𝑀𝑖=1
𝑀∗𝑁 (2)
The root mean square error (RMSE) is calculated from root of MSE then PSNR as-
Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
© www.ijarcsse.com, All Rights Reserved Page | 368
𝑃𝑆𝑁𝑅 = 20. log10(max (𝑌 𝑖,𝑗 )
𝑅𝑀𝑆𝐸) (3)
Here, X (i, j) is input image having M by N pixels, Y(i, j) is enhanced image. Greater the PSNR better will be contrast
of enhanced image.
C. Signal to Noise Ratio (SNR):
Consider r(x,y) be the original image and t(x,y) is enhanced image. The noise estimation in enhanced fundus image is
analyzed by-
𝑆𝑁𝑅 = 10. log10 𝑟 (𝑥,𝑦) 2
𝑛𝑦 −1
0𝑛𝑥−10
1
𝑛𝑥𝑛𝑦 𝑟 𝑥 ,𝑦 −𝑡(𝑥 ,𝑦) 2
𝑛𝑦−1
0𝑛𝑥−10
(4)
VI. COMPARISON OF TECHNIQUES [23]
Sr. No Technique Concept Advantage Disadvantage
1 HE Uniform
distribution of gray
values over scale.
Simple, effective and
low complexity.
Brightness of
an image
2 BBHE Decomposition of
image using mean
value.
Preserve the
brightness of an
image.
Gives an
artificial look
to image.
3 Gray Scale
Grouping
Formation of bin
of grey values.
Applicable to a broad
variety of images.
More
Complex
4 AHE
---- -
AHE tends to over
open up noise in
moderately
homogeneous
districts of a picture.
Complex and
enhances high
contrast area
much more.
Original Image HE AHE BBHE CLAHE
PSNR=3.4406 PSNR=5.1403 PSNR=3.4406 PSNR=4.7401
PSNR=6.5156 PSNR= 6.2626 PSNR=6.5156 PSNR=4.7729
PSNR=5.4274 PSNR=7.1155 PSNR=5.4274 PSNR= 6.3269
PSNR= 6.0605 PSNR=6.2222 PSNR=6.0620 PSNR= 5.9527
Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
© www.ijarcsse.com, All Rights Reserved Page | 369
PSNR=4.8614 PSNR=6.6165 PSNR=4.8613 PSNR=5.2820
VII. CONCLUSION
Medical image is used as the important information and basis for the clinical diagnosis. In order to enhance the
diagnosis quality and accuracy, medical image enhancement is very necessary and also an important research direction of
biomedical engineering. Most commonly used image enhancement method for haze-removing can be applied in medical
images, in this paper review, the enhancement of medical images using efficient algorithms based on HE techniques.
Also evaluate the result of medical image on technique HE, AHE, BBHE and CLAHE to compare the pre-processing of
image.
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