Image Contrast Enhancement using DWT-SVD based ...

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brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality. Keywords—Intensity Exposure Histogram ...
Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number:CFP17AWO-ART, ISBN:978-1-5090-5013-0

Image Contrast Enhancement using DWT-SVD based Masking Technique Sandeepa K S

Basavaraj N Jagadale

J S Bhat

Department of Electronics, Kuvempu University Shimoga, Karnataka, India [email protected]

Department of Electronics, Kuvempu University Shimoga, Karnataka, India [email protected]

Department of Physics, Karnataka University, Karnataka, India [email protected]

Naveen Kumar R

Mukund N Naagund

Department of Electronics, Kuvempu University Shimoga, Karnataka, India [email protected]

Department of Electronics, Kuvempu University Shimoga, Karnataka, India Mukund.n.nargund@christuniversity .in

Abstract: A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality. Keywords—Intensity Exposure Histogram Equalization, Discrete Wavelet Transformation, Reconstruction Approximation, Singular Value Decomposition, Incverse Singular Value Decoposition, Masking technique.

I. INTRODUCTION Image plays an important role in medical, civil, security, astronomy, animation, forensic, web design. Image enhancement is one of the area that helps to improve the image quality and its visual perception. The contrast enhancement and image de-noising are important in medical diagnosis through images of medical applications such as X-Ray, computed tomography (CT), magnetic resonance image (MRI), magnetic resonance angiography (MRA) and position emission tomography (PET), ultrasonic scanning, electrons micrographs etc. [1]. The image enhancement broadly classified into two categories: spatial domain and transform domain. Spatial domain operates on pixel level and transform domain operate on frequency of the images [2]. The spatial domain approach is

Panchaxri SSA Govt First Grade Collede, Ballari Karnataka, India [email protected]

broadly classified as direct and indirect method. In direct method image is improved by using the contrast measures and in indirect method histogram modification technique is used. Indirect methods attract the researchers because of simplicity and computational efficiency [3]. The Histogram Equalization (HE) is the simple contrast enhancement technique but it generates annoying artifacts and intensity saturation effect [4]. To solve these drawbacks sub-image histogram equalization of Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub Image Histogram Equalization (DSIHE) methods are employed to preserve brightness. Some advance method of HE like adaptive histogram, automatic histogram, selective dynamic histogram equalization, threshold optimized histogram equalization, clipping histogram equalization, median mean based Sub image clipped histogram equalization [5-11]. Masking based contrast enhancement, un-sharpe masking technique and wavelet based un-sharpe masking technique have documented in recent study [12]. The proposed method uses DWT, singular value decomposition, intensity exposure histogram equalization and masking techniques effectively to produce maximum contrast enhancement. The organization of the paper as follows, section II presents the proposed method. Experimental results are discussed in section III and section IV concludes the paper. II. PROPOSED METHOD A. Image Decomposition The DWT uses dilation and translation property to decompose the image into four sub band called LL, LH, HL, HH as shown in Fig. 1. The LL sub band contains low frequency coefficients containing illumination information and the other sub band contain high frequency coefficients [13].

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Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number:CFP17AWO-ART, ISBN:978-1-5090-5013-0 C. Masking Technique The masking formulation is achieved by subtracting inverse singular value decomposition and reconstructed approximation coefficient matrix. The ISVD is obtained by applying SVD to both to both original image and reconstructed image, its SVD decompositions are given by the equation (12) and (13) (12) (13) Fig.1 Block diagram of DWT of level 1. The equation (1) is the decomposition of the image as approximation coefficients and equation (2) is the reconstruction of the approximation coefficients obtained from Inverse Discrete Wavelet Transformation (IDWT). (

(

)

)





∑ ∑





(

(

)

)

(

(

Here U1, V1, U2 and V2 are the orthogonal matrices. S1 and S2 are diagonal matrices. The value ᶓ is calculated from the diagonal matrices of original image and reconstructed approximation coefficients matrix as given in equation (14). (

)

( (

)

)

(14)

) (1)

),

(2)

( ) is the approximation coefficient, ( ) is where, the input time domain image with discrete variable x,y with the ( ) is the scale function and ( ) is the size M x N. reconstructed approximation coefficient. B. Intensity Exposure based Histogram Equalization The Intensity Exposure [14] of the image is defined in equation (3) and it is normalized in the range of [0 1]. ( )



(3)

( )

Where ( ) is image histogram and levels The parameter

is total number of grey

can be defined as (

)

(4)

To prevent over enhancement clipping histogram calculated as in the equation (5) and (6). ∑ ( )

( )

(5)

for ( )

(6)

is

( ) , is the clipped histogram and it is computationally efficient [15].

The Probability Density Function (PDF) and Cumulative Distribution Function (CDF) can be defined as ( ) ( ) ∑

( )

( )

(9) (10)

The transfer function ( ) for intensity exposure based histogram equalization is then defined as ( ) ( ) (11)

Fig 2. Block diagram of the proposed method

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Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number:CFP17AWO-ART, ISBN:978-1-5090-5013-0 The any changes in the S1 singular matrix will affect the image intensity. The most promising approach to image contrast enhancement is altering the image intensity information store in S matrix [16]. The new image is obtained by Inverse singular value decomposition is given in equation (15). (15) The ISVD is intensity manipulated image obtained by manipulating using value. After the image decomposition, all illumination information available in LL subband, so we reconstruct only approximation coefficients. The mask formulation used to obtain contrast residual between ISVD and reconstructed approximation coefficients and added with intensity exposure histogram equalization image as shown in fig. 2, to get output image with improved contrast.

Fig.3. Result of Brain MRI image: (a) Original image, (b) HE,(c) BBHE, (d) DSIHE (e) Proposed method

III. EXPERIMENTAL RESULTS AND DISCUSSION In this section, the performance of the proposed method has analyzed and compared with existing histogram equalization methods like HE, BBHE and DSIHE, by using different type of medical MRI images. To evaluate the quantitative performance of proposed method is tested and compared on the basis of peak signal to noise ratio and absolute mean brightness error values and entropy calculation. To check the produced noise artifacts and over enhancement, PSNR values are calculated using equation (16), PSNR represents a measure of the peak error between original and enhanced image (

)

(16) )

((

)

Fig.4. Result of Spine MRI image: (a) Original image, (b) HE,(c) BBHE, (d) DSIHE (e) Proposed method

(17)

The max value is the maximum intensity value of the grey scale image. The mean square error is the sum of the squared error between original and enhanced image. The performance of the brightness preservation is measured on the basis of AMBE, which define absolute gray level mean between original and enhanced image |

̂|

(18)

Where and ̂ are green level mean of the original and enhanced image respectively. Entropy means average information content and the measure of richness of image details. Equation (19) defines entropy. ( )



( )

( )

(19)

Fig.5. Result of Chest image: (a) Original image, (b) HE,(c) BBHE, (d) DSIHE (e) Proposed method Where ( ), is probability density function at the intensity level and is total number of grey levels of the image.

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Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number:CFP17AWO-ART, ISBN:978-1-5090-5013-0

TABLE I.

PSNR VALUES OF TESTED IMAGES

IMAGES

Fig.6. Result of Coronal Tumor image: (a) Original image, (b) HE,(c) BBHE, (d) DSIHE (e) Proposed method

HE

BBHE

DSIHE

PROPOSED

Brain-1

8.727

15.937

17.757

18.484

Skull

2.839

15.834

18.332

30.593

Spine

7.159

14.862

14.970

15.405

chest

16.918

17.062

17.427

18.127

Brain-2

6.886

16.304

11.283

27.852

Brain-3

8.444

16.637

15.616

21.409

T2 MRI axial

9.191

16.047

17.198

22.451

10.251

13.066

26.050

17.201

Normal-Axial

9.250

17.283

19.196

21.390

Average

8.852

15.892

17.537

21.435

Coronal

TABLE II.

AMBE VALUES OF TESTED IMAGES

IMAGES

HE

BBHE

DSIHE

PROPOSED

Brain-1

88.697

29.990

31.081

21.172

Skull

57.022

36.048

22.294

5.770

Spine

102.870

27.700

42.314

30.503

chest

11.601

9.941

4.390

29.735

Brain-2

112.670

33.339

63.747

4.803

Brain-3

91.948

30.779

38.635

13.983

T2 MRI axial

80.087

31.246

31.349

13.304

Coronal

68.121

37.644

8.789

27.309

The performance of the proposed method was observed using Brain images, skull, spine, chest, Brain_Meningioma_Radiology, Brain_mri_transversal, T2 MRI axial image, Normal-Axial-Level2, coronal tumor. Fig. 3, 4, 5 and 6 shows the visual information and contrast enhancement of various techniques like HE, BBHE, DSIHE and proposed method. The concrete result in terms of contrast Enhancement can clearly observe in all images.

Normal-Axial

73.355

16.122

20.986

10.574

Average

76.263

28.090

29.287

17.461

Brain-1

6.293

The visual quality in fig 2 and 3 show the reduced contrast and not equalized enhancement result of HE, BBHE and DSIHE. The coronal tumor image enhancement is clearly visible in the proposed method image but other methods enhance the image more in the left side of septum pellucidum. The step by step of the algorithm implementation like original image, IEHE image, ISVD image, mask image and contrast enhanced output image can see in Fig. 7 of skull image.

Skull

Fig.7. Result of skull image: (a) Original image, (b) IEHE,(c) RA, (d) ISVD (e) Mask image (f) output image

The table I contains the result of PSNR shows that proposed method have improvement over the previous methods in terms of noise artifact. The performance of brightness preservation studied and tabulated in table II in terms of AMBE value. The DSIHE method gives better AMBE value for chest and coronal tumor image but its visual information

TABLE III.

ENTROPY VALUES OF TESTED IMAGES BBHE

DSIHE

PROPOS ED

5.052

5.899

5.919

6.181

6.397

5.016

6.138

6.116

6.362

Spine

5.950

5.118

5.590

5.654

5.843

chest

7.280

5.980

7.157

7.173

7.274

Brain-2

4.486

3.420

4.206

4.070

4.487

Brain-3 T2 MRI axial

5.736

4.488

5.367

5.298

5.621

6.139

4.919

5.788

5.732

6.008

Coronal NormalAxial

6.703

5.726

6.441

6.451

6.671

6.264

5.294

6.021

6.001

6.267

Average

6.139

5.001

5.845

5.824

6.079

IMAGES

INPUT ENTROPY

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Proceedings of the 2nd International Conference on Communication and Electronics Systems (ICCES 2017) IEEE Xplore Compliant - Part Number:CFP17AWO-ART, ISBN:978-1-5090-5013-0 and entropy values not as good as proposed method. In most of the cases, entropy value is closer to original image guarantees of having maximum information content as compared to other methods given in table III. The proposed method enhances medical image by preserving brightness and average information content by minimizing noise. IV. CONCLUSION The proposed method plays important role in medical image contrast enhancement, which consist of two steps. In the first step it enhances the contrast based intensity exposure histogram equalization and second step uses masking formulation based SVD method. The mask images take away actual intensity information between ISVD and reconstructed approximation coefficient. Both steps help to preserve mean brightness and average information of the input image. The mask image added to IEHE image, produces maximum contrast enhancement. The supremacy of the proposed method over other methods proved in terms of visual analysis and experimental results. The improved PSNR values shows enhanced image free from noise factors which cause unnatural look, AMBE indicates mean brightness preservation and entropy value gives the preservation of average information REFERENCES [1]

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