Improvement of Contrast Enhancement Technique for Cervical Cell of Pap Smear Images by Reducing the Effect of Unwanted Background Information

Transcription

1 Improvement of Contrast Enhancement Technique for Cervical Cell of Pap Smear Images by Reducing the Effect of Unwanted Background Information * Nor Ashidi Mat Isa, Nazahah Mustafa, Kamal Zuhairi Zamli, Mohd Yusoff Mashor Medical Imaging Research Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia. * Tel : , Fax : , ashidi@eng.usm.my Abstract - Pap test is probably the most common screening method for cervical cancer. In general, the test is performed by examining the cells using a microscope. While Pap test undoubtedly facilitates diagnosis, it suffers from a number of weaknesses such as blurriness as well as the effects of unwanted noises, which may lead to false diagnosis. In order to address some of these problems, this study discusses a novel image processing approach involving segmentation and contrast enhancement techniques. Firstly, images of cervical cells on the Pap smears are selected using the segmentation technique. Then, four algorithms of contrast enhancement technique are applied on the selected cell to increase their contrast. The results show that the combination of the proposed technique has successfully increased the contrast of cervical cell of Pap smear images. I. INTRODUCTION Nowadays, image processing is used in many diverse application areas. Image processing is used to enhance the quality and contrast of medical images. Contrast enhancement [1]-[3], image segmentation [4]-[7] and image extraction [8] are some of the processing techniques, which can be applied on image to overcome problems faced by pathologists and radiologists. Contrast enhancement is so far the best technique to increase the contrast of poor image. This technique is able to maintain all-important characteristics of an image in order to reduce error in diagnosis resulting from poor images. A few studies have proven that clearer and cleaner images could be obtained using this technique thus assist pathologists and radiologists in screening processes. Pap test is the most popular and effective screening test to detect cervical cancer. Pathologists can classify cervical cells based on Bethesda system by observing the morphological changes in cervical cells [9]-[10]. In the case when the contrast of the cytology images seen under light microscopy is low (i.e. due to unwanted noise such as blood stains, water and etc [11]-[12]), pathologists often have difficulties in making decisive diagnosis. In such a situation, a Pap smear image captured by camera could be used to facilitate diagnosis. A Pap smear image usually consists of three structures. The structures are nucleus, cytoplasm and background area. These structures are often easily seen but in some cases the cells are hazy and afflicted by unwanted noise. These problems can hide and obscure the important cervical cells morphologies, leading to risk of false diagnosis. A few processing techniques such as contrast enhancement and image segmentation have been applied on the Pap smear image to overcome the problems. One of the previous studies applied moving k-means (MKM) clustering algorithm to segment the cervical cells and linear contrast algorithm to enhance the contrast of the cell images [3]. Another study has proposed automated seed based region growing and region growing based feature extraction to segment and extract the cervical cell respectively [8]. This current study proposes a novel approach contrast enhancement methodology for Pap smear images by selecting a cervical cell of interest from a Pap smear image and enhancing the contrast of the preselected cervical cell. II. METHODOLOGY The proposed technique includes two image processing techniques, which are: 1) Selection of cervical cell of interest 2) Contrast enhancement processes During the screening process of cervical precancerous cells, pathologists observe the abnormalities associated to cervical cells. These characteristics include: 1) abnormal size and shape of cells 2) abnormal biological changes in nucleus and cytoplasm These characteristics play important roles in determining the type of cervical precancerous cells. By providing better quality Pap smear images using the contrast enhancement process, the screening process becomes easier.

2 Most of the conventional contrast enhancement applied the technique to the whole Pap smear images, even though the features in the background areas are insignificant in the screening process [3][6][7][13]-[14]. As compared to studies published before, this paper will focus on enhancing the contrast of the cervical cell of interest and ignoring the background areas. This will minimize the influence of the background image pixels on the enhancement algorithms. The proposed contrast enhancement technique will first, determine the cervical cell of interest. Edge detection is one of the image processing techniques that can be employed for this purpose. Many robust and complex edge detection techniques have been proposed [6][7][14]. Conventionally, there are two categories of isotropic edge detectors, namely, the gradient operators and second derivative operators. Gradient operators, such as Prewitt, Roberts and Sobel operators, detect edges by looking for the maximum and minimum in the first derivative of the luminance of an image. The second derivative operators, such as Laplacian operator, search for zero-crossings in the second derivative of the luminance of an image to find the edges. Although both gradient and second derivative operators are simple and easy to implement, these operators offers several disadvantages. Therefore, seed based region growing (SBRG) algorithm has been proposed by Romberg et al. [14] as edge detection method. The study proved that unlike gradient and second derivative methods, the borders (edges) of regions found by SBRG algorithm is perfectly thin and connected. Thus, the size and shape of the regions will not be corrupted. The SBRG algorithm is also very stable with respect to noise. This study utilizes the potential of the SBRG algorithm to select the cervical cells of interest from the Pap smear images. A. Seed Based Region Growing Algorithm In the SBRG algorithm, user needs to determine the unwanted background by selecting any pixel in the region. The user also needs to determine the threshold value. The algorithm of the SBRG can be implemented as [14]: 1. Apply three preprocessing techniques to the image, which are median filter, histogram normalization and histogram equalization. 2. Determine the threshold value. 3. Click mouse in the unwanted background e.g. as shown in Figure 1. (Note: The pixel, which mouse is clicked on it will be used as initial seed pixel). Fig. 1: The location of initial seed pixel. 4. Choose N N neighbourhood as shown in Figure 2 (for N = 7) (Note: Initial seed point must be located at the center of all its neighbours and N must be an odd number) Neighbour pixel Fig. 2: Location of initial seed pixel and its 7 7 neighbourhood. 5. Calculate the mean value, x (which is known as region mean) and standard deviation, σ of the N N neighbourhood using Equation 1 and 2 respectively. n _ i= x = 1 n x i Initial seed point Seed pixel (1) n _ xi x i= 1 σ = + (2) n 1 x i is grey level of i-th pixel in the N N neighbourhood and n is total of pixels in N N neighbourhood. 6. Grow the seed pixels to its neighbour s pixels. Compare the grey level of the seed pixel with its neighbour s pixel. Include the neighbour pixel into the unwanted background region if it satisfy one of the conditions listed below:,: a. If the gradient of the pixel is less than 95% of the equalized histogram AND the grey level of the pixel is greater or equal to the preselected threshold. 2

3 b. If the gradient of the pixel is more than or equal to 95% of the equalized histogram AND the grey level of the pixel is not more than or equal to one standard deviation away from the region mean. 7. Set the neighbour pixel, which is added to the region in Step (6) as a new seed pixel. 8. Repeat Step (5) to (7) until all pixels have been considered to be grown or the pixel cannot be grown anymore. 9. Change the grey level of the pixels, which are included in the unwanted region to 255. (Note: After implementing Step 9, only the cervical cell of interest will have original grey level values, while the grey level of background will be changed to 255 (which is white) e.g. as shown in Figure 3). Fig. 3: Result for determination of cervical cell of interest after applying SBRG algorithm. B. Contrast Enhancement Algorithms The proposed technique will then apply contrast enhancement techniques to enhance the contrast of the preselected cervical cells. Two new contrast enhancement techniques are proposed in this study, namely nonlinear bright and nonlinear dark contrast enhancement techniques. While, two conventional contrast enhancement techniques; namely histogram equalization and linear contrast, are applied as comparison. In some Pap smear images, the digitization process does not fully utilize the dynamic range that is available in digital images, which produce low contrast Pap smear images. This paper proposes contrast enhancement techniques to be applied to the preselected cervical cells. The techniques increase the contrast of images by spreading out the grey levels over the whole possible range (0 to 255). Histogram Equalization The first algorithm is histogram equalization. The technique uniformly redistributes the level values of the pixels within an image so that the number of pixels at any grey level is about the same. The grey level transformation to histogram equalize an image is given by Equation 3. g i = i m 1 nt j= 0 ni (3) (3) n t is total number of pixels in image n i is number of pixels at grey level i m is total number of grey levels possible Linear Contrast Enhancement The second conventional contrast enhancement technique is linear contrast algorithm. Generally, the distribution of grey level for an original Pap smear image lies in limited range, which result low contrast of the image. Linear contrast enhancement algorithm will spread out the distribution of grey level over the whole possible range of histogram (0 to 255) linearly. Figure 4 and show the grey level distribution of an image before and after implementation of linear contrast respectively. Determination of the maximum, t and minimum, s grey level value of original image must be done before implementing the algorithm. The equation for linear algorithm is given by: xi b fi = 255 * (4) a b f i (i, is new grey level of pixel i x i (i, is original grey level value of pixel i a is maximum grey level value of image b is minimum grey level value of image 0 a b 255 Fig. 4: Distribution of grey level original distribution distribution after implementing linear contrast enhancement. Nonlinear Bright Contrast Enhancement Sometime pathologists only need to observe certain area of Pap smear image. For example observation on biological changes of cytoplasm structure, which has brighter grey level than nucleus are. In this case, this study proposes nonlinear bright contrast enhancement technique. This technique will spread out the grey level distribution to the whole histogram nonlinearly with the pixels frequency distribution on the left (dark area) is higher compared to the other side. Hence, this technique reduces the contrast of the dark area and at the same time increases the contrast of the opposite area (bright area). Figure 5 shows the grey level distribution of an image before and after

4 implementation of nonlinear bright contrast. The following equation is used to implement the technique. xi( i, b x fi = 255 * a b (5) r f i (i, is new grey level of pixel i x i (i, is original grey level value of pixel i a is maximum grey level value of image b is minimum grey level value of image r is constant value between a b Fig. 6: Distribution of grey level original distribution distribution after implementing nonlinear dark contrast enhancement. III. RESULT 0 a b Fig. 5: Distribution of grey level original distribution distribution after implementing nonlinear bright contrast enhancement. Nonlinear Dark Contrast Enhancement Besides, pathologists also need to observe the biological changes in the nucleus. In order to enhance the nucleus area, this study proposes a nonlinear dark contrast enhancement. This technique will spread out the grey level distribution to the whole histogram nonlinearly with the pixels frequency distribution on the right (bright area) is higher compared to the other side. Hence, this technique reduces the contrast of the bright area and at the same time increases the contrast of the opposite area (dark area). Figure 6 shows the grey level distribution of an image before and after implementation of nonlinear bright contrast. The following equation is used to implement the technique. a b a xi( i, r r fi = 255 * a b (6) r f i (i, is new grey level of pixel i x i (i, is original grey level value of pixel i a is maximum grey level value of image b is minimum grey level value of image r is constant value between The proposed contrast enhancement technique was applied on five Pap smear images namely Pap004.bmp, Pap005.bmp and Pap006.bmp. The results for Pap004.bmp, Pap005.bmp and Pap006.bmp are shown in Figure 7, 8 and 9 respectively. For each figure: i. Image shows the original Pap smear image ii. Image shows the preselected cervical cell image iii. Image (c) shows the contrast enhancement result after applying histogram equalization to the whole image. iv. Image (d) shows the contrast enhancement result after applying histogram equalization to the preselected cervical cell. v. Image (e) shows the contrast enhancement result after applying linear contrast enhancement to the whole image vi. Image (f) shows the contrast enhancement result after applying linear contrast enhancement to the preselected cervical cell vii. Image (g) shows the contrast enhancement result after applying nonlinear bright contrast enhancement to the whole image viii. Image (h): shows the contrast enhancement result after applying nonlinear bright contrast enhancement to the preselected cervical cell. ix. Image (i) shows the contrast enhancement result after applying nonlinear dark contrast enhancement to the whole image. x. Image ( shows the contrast enhancement result after applying nonlinear dark contrast enhancement to the preselected cervical cell. Table 1 shows the values for threshold and constant, r for both nonlinear bright and dark contrast enhancement technique for all images. TABLE 1 THRESHOLD VALUE AND CONSTANT, r FOR BOTH NONLINEAR BRIGHT AND DARK CONTRAST ENHANCEMENT Image Threshold r (bright) r (dark) Pap004.bmp Pap005.bmp Pap006.bmp

5 (e) (f) (c) (d) (g) (h) (e) (f) (i) ( Fig. 8: Results of contrast enhancement for Pap005.bmp (g) (h) (i) ( Fig. 7: Results of contrast enhancement for Pap004.bmp (c) (d) (e) (f) (c) (d) (g) (h)

6 (i) ( Fig. 9: Results of contrast enhancement for Pap006.bmp IV. DISCUSSION Consider the result for selection of cervical cells of interest as shown in image in Figure 7 to 9. The result obtained for each Pap smear image shows that SBRG algorithm has successfully produced good edge detection performance. Through this technique the unwanted background of the Pap smear image has been removed. The fact that the unwanted background of the Pap smear image has been removed can help pathologists concentrate on the cervical area (nucleus and cytoplasm) in order to undertake the required diagnosis. The SBRG algorithm has successfully detected only the edges of the cervical cells and any changes of grey level in the cells are ignored. In addition, it also creates perfectly thin, smooth and fully connected edges. Moreover by implementing preprocessing together with this technique, clearer and cleaner image can be provided. The advantage of using the SBRG algorithm is that the original size and shape of the cervical cells of interest are maintained. For contrast enhancement in the second stage, histogram equalization was then first applied to the preselected cervical cell. The results for this technique are shown in image (d) for Figure 7 to 9. The results show that the proposed contrast enhancement technique produces good contrast enhancement performance. The biological changes of grey level, size and shape of cytoplasm can be clearly seen but not nucleus. As can be seen, the texture of the cytoplasm that has been enhanced through this algorithm looked harshly granules with nucleus area becomes dark. As compared to the result of conventional technique (in image (c) for each Figure 7 to 9), the contrast of background areas was not enhanced as in the proposed technique. The proposed technique also produces better contrast enhancement result by reducing the influence of background on the cervical cells of interest. The second contrast enhancement technique applied to the preselected cervical cell was linear contrast. The results for this technique are shown in image (f) for each Figure 7 to 9. This technique also produces good contrast performance. The grey level range of those preselected cervical cells has been spread successfully throughout the full grey level dynamic range, significantly increasing their contrast. Therefore both biological changes of nucleus and cytoplasm can be clearly seen. By comparing image between (e) and (f) (result for conventional linear contrast transformation image and linear contrast transformation of preselected cervical cell image respectively) for each figure (Figures 7 to 9), the results clearly show that the proposed technique produces better contrast enhancement result by reducing the influence of background on the cervical cells of interest. The third contrast enhancement technique was nonlinear bright contrast enhancement. This technique is purposely proposed to enhance the biological changes of the cytoplasm area of cervical cell. The result of this algorithm can be seen in image (h) for each Figure 7 to Figure 9. The grey level range on the right side of histogram have been spreaded successfully throughout the full grey level dynamic range, significantly increasing contrast of that area. Hence, the biological changes only can be clearly seen in cytoplasm but not in nucleus because cytoplasm grey level distribution lies to the most right of histogram compared to nucleus. As compared to the result of applying nonlinear bright contrast to the whole Pap smear image (as shown in image (g) for each Figures 7 to 9), the technique enhanced the contrast of the unwanted background area better than the cytoplasm area. Thus, the contrast of the background area is higher than the cytoplasm area. This can make any observation on that cervical cell will be influenced by the background areas. From the results, it clearly proved that better contrast enhancement result could be produced using the proposed technique because only contrast on the preselected area (i.e. cytoplasm area) will be enhanced as well as reducing the effect of unwanted background area. The second contrast enhancement technique proposed was nonlinear dark contrast enhancement. This technique is purposely proposed to enhance the biological changes of the nucleus area of cervical cell. Image ( for Figures 7 to 9 show the results for nonlinear dark contrast. Through this algorithm, the grey level range on the left side of histogram have been spreaded successfully throughout the full grey level dynamic range, significantly increasing contrast of that area. This is opposite to the nonlinear bright contrast the biological changes only can be clearly seen in nucleus but not in cytoplasm because nucleus grey level distribution lie to the most right of histogram compared to cytoplasm. Any biological changes of grey level, size and shape in nucleus easily can be seen after this algorithm has been applied on the preselected Pap smear image.

7 V. CONCLUSION The proposed technique improves the image quality so that the resultant image produced would be more useful for further analysis by pathologists. Combination of segmentation and contrast enhancement technique are effective to increase contrast on the selected area of cervical cell. There is a reduction in the influence of image background on particular cervical cell of interest, hence producing cleaner and clearer images to be observed by pathologists. Further testing will be performed on larger sets of Pap smear images in order to ascertain the suitability of the proposed technique. REFERENCES [1] N. E. A. Khalid, P. A. Venkatachalam, & U. K. Ngah. Diagnosis of Bone Lesion Based on Histogram Equalization. Proc. of Int. Conf. on Robotics, Vision and Parallel Processing for Automation. pp [2] U. K. Ngah, T. H. Ooi, S. N. Sulaiman, & P. A. Venkatachalam. Embedded Enhancement Image Processing Techniques on A Demarcated Seed Based Grown Region. Proc. of Kuala Lumpur Int. Conf. on Biomedical Engineering. pp [3] N. A. Mat-Isa, M. Y. Mashor & N. H. Othman. Contrast Enhancement Image Processing Technique on Segmented Pap Smear Cytology Images, CDROM Proceedings of World Congress on Medical Physics and Biomedical Engineering (WC2003). paper no Vol. 4. Sydney, Australia. pp August [4] C. W. Chen, J. Luo & K. J. Parker. Image Segmentation via Adaptive K-Mean Clustering and Knowledge-Based Morphological Operation with Biomedical Application. IEEE Trans. on Image Processing. Vol. 7. No. 12. pp [5] T. H. Ooi, U. K. Ngah, N. E. A. Khalid & P. A. Venkatachalam. Mammagraphic Calcification Cluster Using The Growing Region Technique. Proc. of The New Millennium Int. Conf. on Pattern Recognition, Image Processing and Robot Vision. pp [6] J. P. Fan, D. K. Yau, A. K. Elmagarmid & W. G. Aref. Automatic Image Segmentation by Integrating Colour-Edge Extraction and Seeded Region Growing. IEEE Trans. on Image Processing. Vol. 10. No. 10. pp [7] S. D. D. V. Rughooputh & H. C. S. Rughooputh. Image Segmentation of Living Cells. Proc. of Int. Conf. on Robotics, Vision and Parallel Processing for Automation. Vol. 1. pp [8] N. A. Mat-Isa. Early Diagnosis System for Cervical Cancer Based On Neural Networks. PhD thesis, University Science of Malaysia [9] T. S. Kuie. Cervical Cancer: Its Causes and Prevention. Singapore: Times Book Int [10] HTAC. Pap Smears and Prevention of Cervical Cancer. Citing from internet source URL [11] N. H. Othman, M. C. Ayub, W. A. A. Aziz, M. Muda, R. Wahid & S. Selvarajan. Pap Smears Is It An Effective Screening Methods for Cervical Cancer Neoplasia? An Experience with 2289 Cases. The Malaysian Journal of Medical Sciences. Vol. 4. No. 1. pp [12] T. G. Hislop, P. R. Band, M. Deschamps, H. F. Clarke, J. M. Smith & V. T. Y. Ng. Cervical Cancer Screening in Canadian Native Women: Adequacy of The Papanicolaou Smear. The J. of Clinical Cytology and Cytopathology. Vol. 38. No. 1. pp [13] D. Anoraganingrum. Cell Segmentation with Median Filter and Mathematical Morphology Operation. Proc. of Int. Conf. on Image Analysis and Processing. pp [14] J. Romberg, W. Akram & J. Gamiz (1997). Image Segmentation Using Region Growing. WDEKnow/index (2005).