Multiscale based adaptive contrast enhancement

Multiscale based adaptive contrast enhancement Muhammad Abir*a, Fahima Islama, Daniel Wachsb, Hyoung Leea a Nuclear Engineering, Missouri University of Science and Technology, 301 W. 14th St., Rolla, MO bMaterials and Fuels Complex, P.O. Box 1625, Idaho National Laboratory, Idaho Falls, ID ABSTRACT A contrast enhancement algorithm is developed for enhancing the contrast of x-ray images. The algorithm is based on Laplacian pyramid image processing technique. The image is decomposed into three frequency sub-bands- low, medium, and high. Each sub-band contains different frequency information of the image. The detail structure of the image lies on the high frequency sub-band and the overall structure lies on the low frequency sub-band. Apparently it is difficult to extract detail structure from the high frequency sub-bands. Enhancement of the detail structures is necessary in order to find out the calcifications on the mammograms, cracks on any object such as fuel plate, etc. In our proposed method contrast enhancement is achieved from high and medium frequency sub-band images by decomposing the image based on multi-scale Laplacian pyramid and enhancing contrast by suitable image processing. Standard Deviation-based Modified Adaptive contrast enhancement (SDMACE) technique is applied to enhance the low-contrast information on the sub-bands without overshooting noise. An alpha-trimmed mean filter is used in SDMACE for sharpness enhancement. After modifying all sub-band images, the final image is derived from reconstruction of the sub-band images from lower resolution level to upper resolution level including the residual image. To demonstrate the effectiveness of the algorithm an x-ray of a fuel plate and two mammograms are analyzed. Subjective evaluation is performed to evaluate the effectiveness of the algorithm. The proposed algorithm is compared with the well-known contrast limited adaptive histogram equalization (CLAHE) algorithm. Experimental results prove that the proposed algorithm offers improved contrast of the x-ray images. Keywords: Laplacian Pyramid, Adaptive Contrast Enhancement, X-ray Images, Standard Deviation.

1. INTRODUCTION Breast cancer is a heterogeneous progressive asymptomatic disease of adult males and females. It develops from within the branching ductal system of the breast. About 1 in 8 adult females (about 12%) in the United States develops invasive breast cancer in her lifespan 5. Breast cancer can be developed in different areas of the breast: the ducts, the lobules, or even the tissue in between. Recently, digital mammography became superior to film mammography due to lower radiation dose, wider dynamic range, and better image quality by virtue of digital image processing. However, the contrast in raw images from a digital mammography detector is very poor due to low subject contrast of breast tissues and narrow distribution of pixel values as opposed to the much wider dynamic range of the detector. Therefore, to avoid misinterpretation of the characteristic features in a breast due to that low-contrast and noise, the mammogram must be displayed with optimal contrast3. X-ray examination is performed in industry to find internal cracks and voids in an object. Due to the low frequency overall structure of the object it is difficult to distinguish cracks and voids on the object. Contrast enhancement can improve the contrast of the image which will assist to identify the cracks and voids from the overall structure of the object. While image contrast can be enhanced either by global or local enhancement techniques, the latter is preferred widely due to the large image size and very narrow histogram of the pixel values in the raw image12. Moreover, improved contrast tends to increase noise, which is not desirable. Therefore, there is always a trade-off between contrast improvements and noise on the image. Sharpness is another factor that tends to reduce with enhancement of contrast.

*[email protected]; phone 1 573 261-0891; web.mst.edu/~leehk

Computational Imaging XI, edited by Charles A. Bouman, Ilya Pollak, Patrick J. Wolfe, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 8657, 86570X • © 2013 SPIE-IS&T CCC code: 0277-786X/13/$18 • doi: 10.1117/12.2005567 SPIE-IS&T/ Vol. 8657 86570X-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/22/2013 Terms of Use: http://spiedl.org/terms

Several methods have previously been developed for the contrast enhancement of x-ray images. Adaptive histogram equalization (AHE) gives suitable contrast, but it amplifies the noise within the homogenous dense-tissue areas on the mammogram and background3. To reduce the noise amplification, contrast-limited adaptive histogram equalization (CLAHE) gives a promising result for enhancement through the adaptive nature of its algorithm. The limitation of CLAHE is that the original intensity might be lost, and it might simulate calcification on the mammogram by enhancing the visibility of nuisance information17. Agaian et al presented novel medical-image enhancement algorithm for enhancing electron-microscope images, computed tomograph (CT) scans, magnetic resonance imaging (MRI) scans, and radiological images. They used a cascaded unsharp masking algorithm using a modified adaptive contrast enhancement algorithm2. Dhawan et al proposed an optimal adaptive-neighborhood image-processing technique that would measure local contrast by selecting a neighborhood around a given pixel and enhancing its contrast by suitable contrast enhancement function9. Sakellaropoulos et al presented multiscale-based wavelet technique to enhance the contrast and suppress the noise19. In their method, they decomposed an image into multiscale sub-band images and enhanced contrast by applying a linear mapping operator on denoised wavelet coefficients. Laine et al enhances the coefficients of subband images locally and globally by nonlinear mapping function and multiscale adaptive gain for enhancing the subband images13. Burt and Adelson first implemented a Laplacian pyramid for image compression 6. Chan et al presented the effect of Laplacian-pyramid compression for detecting micro-calcification of mammograms8. Vuylsteke et al presented multiscale image-contrast amplification (MUSICA) based on the Laplacian pyramid; their process decomposes the image into sub-band images by applying a low-pass filter and subsampled the image by a factor of two20. It enhances the contrast by modifying the Laplacian coefficients by using non-linear amplification. Dippel et al have shown that the Laplacian pyramid is more suitable over wavelet transform because wavelet transform generates visible artifacts due to enhancement of the large structures10. The Laplacian pyramid is a well-known multi-resolution-based image-enhancement technique. MUSICA utilizes the Laplacian pyramid to decompose an image into several frequency sub-bands. All the sub-band images are then passed through a non-linear mapping function that enhances both the high- and low-frequency sub-bands. The enhanced subbands are then combined by applying an inverse decomposition technique to obtain the desired, enhanced image. This article presents a new approach for image enhancement based on the Laplacian pyramid and standard-deviationbased modified adaptive-contrast enhancement (SDMACE) technique. We fused the Laplacian pyramid and SDMACE techniques. The algorithm gives better control of contrast enhancement as well as reduced noise enhancement and preserve sharpness in the image. The paper is organized in the following way: Section 2 describes some basic methods and enhancement techniques that are related to this work. Sections 3 introduce our proposed algorithm. Section 4 compares our proposed algorithm with CLAHE algorithms and evaluates our algorithm using subjective evaluation. Section 5 represents the concluding remarks.

2. BACKGROUND Our proposed algorithm utilizes the Laplacian pyramid and Standard Deviation based Adaptive Contrast Enhancement (SDACE) algorithm. In this section we review Laplacian pyramid, SDACE, MUSICA, CLAHE, and modified non-linear alpha-trimmed mean filter 2.1 Laplacian pyramid Burt and Adelson first proposed the Laplacian pyramid algorithm for image compression6. An image consists of two types of scale: coarse scale and fine scale. Scale corresponds to the amount of detail and resolution corresponds to the size of detail that can be perceived by an observer 4. The original shapes and general features lie on the coarse scales while detail structures and indistinguishable features lie on fine scales on the image1. Laplacian pyramid decomposes an image into several resolutions that makes the image possible to analyze in different scales. The original image is first convolved with a Gaussian type low-pass filter and then subsampled by a factor of two. The convolved image is subtracted from the original image to get the Laplacian image. The filtering-subsampling operation repeats several times in order to obtain the Laplacian pyramid. The reconstruction of the Laplacian image is achieved by interpolating the Laplacian image and adding with the previous resolution level filtered image. The sums of all interpolated images adding with the previous resolution level images repeat till the resultant final image is obtained.

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2.2 Adaptive contrast enhancement based on local standard deviation Chang et al presented adaptive contrast enhancement based on local standard deviation (LSD) which utilizes contrast gain to enhance the high frequency components7. The algorithm states that it can eliminate noise overenhancement and ringing artifacts of an image by manipulating the high, medium and low frequency components differently. LSD formulates contrast gain (CG) that can adopt larger values for medium spatial frequencies and smaller value for high and low spatial frequencies that reduces overenhancement and ringing artifacts. The modified contrast gain is formulated as7: (1) where

The term x(i,j) is the gray value of a specific pixel where (2n+1)×(2n+1) is the window centered at (i,j) and n is an integer. is the standard deviation. Eq 2. shows that mx(i,j) will be the mean of the local image x(k,l) surrounded by (2n+1)×(2n+1). K(i,j) is the modified contrast gain that modifies the high, medium and low frequencies. The value of K(i,j) is closer to for the high and low frequencies and larger for medium frequencies. 2.3 Modified alpha trimmed mean filter Alpha trimmed mean filters are well known edge preserving mean filter. This filter provides good compromise between moving average and median filter. It gives the local estimation when the Gaussian data contains some outliers14. Moreover, this non-linear filter is also able to eliminate salt-and-pepper noise2. The alpha trimmed mean filter is defined as follows15: Ordered set: x1(i) ≤ x2(i) ≤ x3(i) ≤ . . .≤ xn(i)

where x1(i) is the minimum, and xn(i) the maximum, signal value. The indicates the percentage of the signal to be trimmed. If is 0.5, the filter is closed to median filter, and if closes to zero, the filter becomes a moving-average filter15. 2.4 Multiscale image contrast amplification (MUSICA) MUSICA utilizes the Laplacian-pyramid technique to enhance contrast of an image using a non-linear transformation function on the frequency sub-band images. MUSICA performs straightforward manipulation on the sub-band images. Non-linear amplification is performed on the sub-band images according to the following formula20:

(4) and

–M < x < M and 0 < xc < M

where x represents the original transform coefficient, y(x) represents the modified transform coefficients, M is the upper bound if the coefficients, a is the global amplification factor, and xc is a transition value that controls the contrast amplification and reasonable noise level. 2.5 Contrast limited adaptive histogram equalization (CLAHE) CLAHE is the modification of adaptive histogram equalization that limits the enhancement to imposing a clip limit on the histogram of local region 16. This reduces the over-enhancement of the noise as well as edge-shadowing effect of


AHE’s algorithm. AHE is based on histogram equalization (HE) that involves histogram mapping on a pixel in its contextual region 18. HE computes the intensity image from the original image. It creates a 1D probability density function (PDF) using the following formula11: (5) where L is the number of possible intensity values (L=256 for 8 bit image). The HE function can be then defined as11

fk restores a new grey value for all intensity values of the original image.

3. MODIFIED ADAPTIVE CONTRAST ENHANCEMENT BASED LAPLACIAN PYRAMID We propose a new image processing method using Laplacian pyramid based modified adaptive contrast enhancement. This new approach will be able to enhance x-ray images without over-enhancement of noise and ringing artifact. 3.1 The Laplacian pyramid and SDMACE The SDMACE algorithm uses Laplacian pyramid framework. The high-, medium-, and low-frequency components are separated by smoothing the original image by a Gaussian-based low-pass filter and subtracting it from the original image. The smoothed image is then subsampled by a factor of two and further smoothed by the Gaussian filter and subtracted.

2↓

L0'

L0 D1 2↓

Modified

D0

LSDACE SDMACE

Original Image, I0

L1 D2 2↓

L2 D3 2↓

L1'

L3' L4

2↓

R1

L2'

L3 D4

R0

L4' Residual Image

R2 R3

R4

Output Image, F

2↑

2↑

2↑

2↑

2↑

Figure 1. Flow diagram of the proposed algorithm The original image, I0, is first smoothed by passing a 5 × 5 Gaussian low-pass filter to produce D0. The first level of Laplacian image, L0, is produced by subtracting D0 from the original image. The resolution of the Laplacian image is the same as the resolution of the original image. The filtered image (D0) is then subsampled by a factor of two and filtered again to produce the second Laplacian image, L1. The resolution of L1 is half of L0. The step repeats till five Laplacian images L0, L1, L2, L3 and L4 are obtained. The resolution of each Laplacian image is half that of its former Laplacian image. Mathematically we can say that (7)


L0 contains mostly the high-frequency detail information, and L4 contains mostly the low-frequency overall structure information of the original grayscale image. L1, L2, and L3 contain medium-frequency information. The image which is not subsampled contains the remaining frequency information of the image and is called the residual image. After separating the high-, medium-, and low-frequency components, the SDMACE technique is applied to enhance Laplacian images. The Laplacian images are enhanced in such a way that it does not enhance the low-frequency components; rather, it enhances the high- and medium-frequency components without enhancing noise and ringing artifact. The SDMACE can be written as (8) and

where is the alpha-trimmed mean filter surrounded by a window around each coefficient of the Laplacian image, K is the modified contrast gain factor, and is the standard deviation of each Laplacian image. The difference between SDACE and modified SDMACE is that we replace the local mean filter with the alpha-trimmed mean filter and choose the global standard deviation of each Laplacian image instead of local standard deviation. The alpha-trimmed mean filter preserves an edge of each Laplacian image that makes the algorithm suitable for edge enhancement and preserving detail structures without distortion. The modified contrast gain K determines the output contrast. If K < , then contrast is reduced; if K = , contrast remains same. If K > , contrast is enhanced. The value of K is kept equal to for the low-frequency components and larger than for high- and medium-frequency components. By setting proper K, contrast of all the components can be manipulated. After manipulation of the Laplacian images, the reconstruction is obtained by inverse of the decomposition operation. The residual image is up-sampled by a factor of two using a bicubic interpolation scheme and filtered by the Gaussian low-pass filter. The low-frequency Laplacian image, , is added with the upsampled smoothed residual image, and the reconstructed image is obtained. The reconstructed image is again upsampled by a factor of two and smoothed again. The upsampled image is added to to obtain the reconstructed image . The process is repeated until the final image F is obtained. The proposed Laplacian-SDMACE algorithm that can process high-, medium-, and low-frequency components separately offers better enhancement of the mammogram without overshooting noise and artifacts. The SDMACE algorithm gives better sharpening on the edges to preserve detailed information of the image. Different types of images, such as neutron radiographs, CT images, or even digital photographs that suffer poor contrast will be benefited by using this algorithm.

4. RESULTS AND DISCUSSION Several mammograms are selected from the Mammographic Image Analysis Society (MIAS) database14. The images are digitized to 50-micron pixel edge, reduced to 200-micron pixel edge, and padded to make the image 1024 × 1024 pixels. The algorithm was developed in MATLAB 7.8.0 (R2009a). The computer used was a core i5 CPU with 4.0 GB memory. Typically, the breast of young women contains dense glandular tissue. The tissue becomes fatty-glandular tissue as women age. The dark areas in the mammogram represent the fatty tissue, and the lighter areas represent dense tissue. Cancerous tumors or calcification appear white on the mammogram and are difficult to distinguish from dense tissue. Two patient mammograms (both breasts) were selected for analysis. Both patients’ mammograms contain dense tissue; thus, it is difficult to differentiate calcification from dense tissue on the mammograms. Table 1 demonstrates the results from the proposed technique and comparison among the original image, the proposed techniques, and CLAHE. It is clearly visible that the proposed technique is superior to CLAHE. The propose algorithm has the flexibility to reach different level of enhancement. By changing the contrast gain K for different levels, proper contrast can be achievable without over-enhancement of noise and artifact. For suitable enhancement we chose K = for low-frequency band, K =


1.5 for high- frequency band and K = 2 for medium-frequency bands. The contrast between fatty tissue and dense tissue is more distinguishable using the proposed technique than in images produced by CLAHE. "a

I I

TIT

Figure 2. Original image

Figure 3. Original image

Figure 4. Proposed method

Figure 5. Proposed method

(left breast)

(right breast)

(left breast)

(right breast)

Figure 2 shows the original left breast image containing dense speculated benign mass, and Figure 3 shows the original right breast image containing dense normal mass. Figures 4 and 5 show the results of the proposed algorithm.

I

Figure 6. CLAHE result (left breast) Figure 6 and Figure 7 are the results of the CLAHE algorithm

Figure 7. CLAHE result (right breast)

An x-ray image of a pre-irradiated nuclear fuel is selected for analyzing cracks on the image. The fuel was developed for the Reduced Enrichment for Research and Test Reactor (RERTR) program at Idaho National Laboratory (INL). The fuel


contains a crack on the right edge side. Figure 14 shows the original x-ray image of the fuel. The crack is barely visible on the original image, but the enhanced image in Figure 15 shows the crack clearly.

1

t

Figure 8. Original image (left breast)

Figure 9. Original image (right breast)

Figure 10. Proposed method (left breast)

Figure 11. Proposed method (right breast)

Figure 8 shows the original breast image (left) containing dense normal mass and Figure 9 shows the original breast image (right) containing dense mass with calcification. Figure 10 and Figure 11 are the enhanced results using proposed algorithm.

Figure 12. CLAHE result Figure 13. CLAHE result (left breast) (right breast) Figure 12 and Figure 13 show the results of the CLAHE algorithm.


I

Figure 14. Original image.

Figure 15. Proposed method.

5.

Figure 16. CLAHE result.

CONCLUSION

We introduced a new image processing scheme that is able to enhance contrast and sharpness as well as reduce noise over-enhancement of radiographs such as mammograms. We utilize Laplacian pyramid to decimate the radiograph into high-, medium-, and low frequency bands. Standard deviation based modified adaptive contrast enhancement (SDMACE) technique has been implemented on the sub-bands to enhance the contrast of the mammogram while reducing the noise over-enhancement and ringing artifacts. An alpha trimmed mean filter is implemented to improve the sharpness of the detail region. The modified contrast gain factor can enhance each frequency sub-band adaptively based on their standard deviation. Finally, the inverse decomposition method is implemented to reconstruct the frequency subbands and produce the final enhanced image. The results of the proposed method prove that the developed algorithm has excellent capability to enhance the mammogram efficiently. The proposed method can be implemented to other radiological images or over neutron radiography.

ACKNOWLEDGEMENT The work is supported by a Faculty Development Award from the University of Missouri Research Board. The authors would like to thank Dr. Ralph Schaetzing for giving valuable comments about the MUSICA algorithm, and Dr. Xin Liu for his cooperation.

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