Medical Image Contrast Enhancement based on Gamma Correction K. Somasundaram1
and P. Kalavathi2
Image Processing Lab, Department of Computer Science and Applications Gandhigram Rural Institute, Gandhigram – 624 302, Tamilnadu, India 1
[email protected] 2
[email protected]
Abstract— The luminance non-linearity introduced by many medical imaging devices often affects the performance of medical image processing techniques. Image enhancement plays an important pre-processing step in medical image processing methods. In this paper, we present a new automatic method for medical image contrast enhancement based on gamma correction. In this method, a global gamma value is calculated based on the image cumulative histogram without any knowledge of the imaging device. We evaluated our method using MR brain images and CT scan images. The performance of the proposed method is compared with three popular contrast enhancement techniques by means by PSNR measure.
image, bringing out more detail in the image at the same time it produce significant noise. In this paper we proposed a new method to enhance the contrast of the medical image based on gamma correction. The gamma value is calculated using cumulative histogram. The performance of our method is evaluated with MRI and CT scan images and is found to give satisfactory results. The remaining part of the paper is organized as follows: In section II, we explained the methods and materials that are used in the proposed approach. In section III, the experimental results and discussion are given. In section IV, the conclusion is given.
Keywords— Contrast enhancement, Image enhancement,
2.1 Methods
Gamma correction, Cumulative Histogram
I.
INTRODUCTION
Many images, such as medical images, remote-sensing images, electron microscopy images and even real photographic pictures, suffer from poor contrast. Therefore, it is necessary to enhance the contrast of such images before applying further processing techniques. There are numerous existing techniques are available in the literature [1]-[5]. Automatic contrast enhancement by global histogram processing is a basic tool for image enhancement. Histogram equalization (HE) is a popular technique for enhancing the contrast of an image. HE is similar to contrast stretching in that it attempts to increase the dynamic range of the pixel values in an image [6]. Its basic idea lies on mapping the gray levels based on the probability distribution of the input gray levels. Due to the flattening property of HE, either it performs over or under enhancement. Intensity adjustment (IA) [7] is used to improve the image brightness. IA is a technique for mapping an image’s intensity values to a new range. The minimum and maximum intensities values are mapped to 0 and 1. Adaptive histogram equalization (AHE) [8] is an automatic enhancement technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the intensity values of the image. AHE is considered an image enhancement technique capable of improving the local contrast of the
II. METHODS AND MATERIALS The luminance non-linearity introduced by the imaging devices can often describes with simple point-wise operation called gamma correction. Gamma correction controls the overall brightness of an image. Trying to enhance the image requires knowledge of gamma. A proper estimation of gamma value enhances the contrast of the image. In this method, gamma value is automatically computed based on the image’s cumulative histogram. The process of gamma correction is shown in Figure 1. The histogram of digital image with the intensity levels in the range [0, L-1] is defined as a discrete function:
his (rk ) = n k
(1)
where
rk is the intensity value, n k is the number of pixels in the image with intensity rk his (rk ) is the histogram of the image with gray level rk The cumulative histogram is the cumulative sum of his (rk ) and is defined as
C (h j ) = where
k j =1
his (r j )
(2)
k is the gray level of an image his (rk ) is the number of pixel in the jth gray level
C(h j ) is the cumulative histogram value for the jth gray level After finding the cumulative histogram, we find the minimum, maximum and median values in the range C5 to C95 in the cumulative histogram C. C5 to C95 are the values that correspond to 5% and 95% of the cumulative histogram of the image, ie., the very low and very high values are ignored for finding the minimum, maximum and median values. Figure 1 (c) represents the finding of minimum, maximum and median values for the sample selected image given in Figure 1(a). Then the gamma value g is computed as:
g = log
median − minimum maximum − median
(3)
We then normalize the g value between 0.8 and 1.2 as given below:
0.8 if g < 0.8 g = 1.2 if g > 1.2 g otherwise
(4)
In this method, we limited the g value to lie between 0.8 and 1.2 after executing our algorithm on several images. Then using the normalized g value, the contrast of the image is enhanced by gamma correction method. The contrast enhanced image G is obtained as: 1 g
(5) G ( x, y ) = ( f ( x, y )) where f(x,y) is the intensity of the input medical image. The normalized gamma curve and the contrast enhanced image for the selected sample image (Figure 1(a)) are shown in Figure1(d) and Figure 1(e) respectively. The steps involved in the proposed method are summarized as follows: Step 1: Let f(x,y) is the intensity values of the input medical image Step 2: Compute the cumulative histogram C for the input image f by eqns. (1) and (2). Step 3: Find minimum, maximum and median values in the range C5 and C95. Step 4: Calculate g using eqn. (3). Step 5: Normalize g value by eqn. (4). Step 6: Apply gamma correction in the input image f using the normalized g value to produce the contrast enhanced image G by eqn. (5). 2.2. Materials We have used 20 volumes of MR brain images obtained from the Internet Brain Segmentation Repository (IBSR) [9] of the Centre for Morphometric Analysis (CMA) at the Massachusetts General Hospital and from the website ‘The Whole Brain Atlas’ (WBA) [10] maintained by the Department of Radiology and Neurology of Brigham and Women’s Hospital, Harvard Medical School, the Library of Medicine, and the American academy of neurology. We have also used 10 CT scan images collected from the internet for our experiments.
Figure 1: Process of gamma correction (a) original image (b) histogram (c) cumulative histogram (d) gamma curve (e) gamma corrected image
2
Each row in Figure 2 represent images numbered from 1 to 8. Images 1 to 6 are MR brain images and images 7 and 8 are CT scan images. To compare the performance of the proposed method, we calculated the PSNR values for the proposed and existing methods and are given in Table. 1. Except for Image 6, the proposed method gave higher PSNR values than HE, IA and AHE methods.
III. RESULTS AND DISCUSSION We carried out experiments by applying the proposed method on the collected images and compared the performance of the new technique with the three popular existing techniques HE, IA and AHE. The selected sample images and the corresponding contrast enhanced images by the proposed and the existing methods are shown in Figure 2.
Figure 2: Results of contrast enhancement. Column 1 original image, column 2 contrast enhanced image by the proposed method, column 3 by HE, column 4 by IA and column 5 AHE methods
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V. ACKNOWLEDGEMENT
TABLE I COMPUTED PSNR VALUES FOR THE SELECTED IMAGES OF FIGURE 2 BY THE PROPOSED, HE, IA AND AHE METHODS
Image Name Image 1 Image 2 Image 3 Image 4 Image 5 Image 6 Image 7 Image 8
Proposed
HE
IA
AHE
34.00 31.36 31.93 29.41 35.43 29.78 31.38 25.17
5.72 8.32 5.63 8.16 7.21 8.62 4.59 15.23
22.68 20.78 23.09 20.86 18.03 28.63 20.41 22.32
23.17 20.80 21.26 19.19 31.63 30.61 21.47 23.12
This work is partly supported by a research grant by University Grants Commission (UGC), New Delhi (Grant no: M.R.P, F.No-37-154/2009(SR)). VI. REFERENCES [1] [2] [3] [4] [5]
IV. CONCLUSION [6]
In this paper, we proposed a new contrast enhancement technique for medical images based on gamma correction method. The gamma value is calculated based on the cumulative histogram. The quality of the enhancement is measured in term of PSNR values and produced higher PSNR than the existing method HE, IA and AHE. The proposed method is a simple and efficient method; it is straight forward to extent this method to general gray scale images. Hence, this method can be used as a preprocessing technique for medical image processing.
[7] [8] [9] [10]
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R.C. Gonzalez, and R.E. Woods, Digital Image Processing, Addison-Wesley Publishing Company (1992). P. Zamperoni, Image Enhancement, Advances in Imaging and Electron Physics, 92, 1-77 (1995). C.C. Sun, S.J. Ruan, M.C. Shie and T.W. Pai, Dynamic Contrast enhancement based on Histogram Specification. IEEE Consum. Electr., 49, 1300-1305 (2005). H. Farid, Blind Inverse Gamma Correction, IEEE Trans. On Image Processing, 10(2) (2001). J. Zummerman, S. Pizer, E. Staab, E. Perry, W. McCartney and B. Brenton, Evaluation for Contrast Enhancement, IEEE Trans. Medical Imageig, 304-312 (1998) S.D. Chen and A.R. Ramli, Minimum Mean Brightness errorBi-histogram equalization in Contrast Enhancement, IEEE Trans. Consum. Electr. Vol 49, no. 4, 1310-1319 (2003). www.mathwork.com S.M. Pizer, Adaptive Histogram Equalization and its Variations, Computer Vision, Graphics and Image Processing, vol 39, no. 3, 355-385 (1987). IBSR data set available online: http://www.cma.mgh.havard.edu/ibsr/index.html WBA (Whole Brain Atlas) MR brain image available online: http://www.med.harvad.edu/AANLIB/home.html
