A Comparative Analysis on Histogram Equalization ... · Many other enhancement methods are...

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

dx.doi.org/10.23956/ijarcsse/V7I6/0269

Singh et al., International Journals of Advanced Research in Computer Science and Software Engineering

ISSN: 2277-128X (Volume-7, Issue-6)


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




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




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-




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




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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