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A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis

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Abstract

Mammography is a tool that uses X-rays to create mammograms. This tool is mainly used to find early signs of breast cancer. Usually, mammogram image contains region with low contrast and complicated structured background. This may cause difficulties in detection of infected cells in their early stage. Using contrast enhancement of mammogram image we can increase the detection rate of early breast cancer. In this paper we propose a tool supported method named histogram modified grey relational analysis, based on HE with local contrast enhancement for mammogram images. This method enhances local as well as global contrast of given mammogram image and segments breast region in order to obtain better visual interpretation, analysis, and classification of mammogram masses to assist radiologists in making more accurate decisions. The main contribution of this work is to show that better breast-region segmentation results can be achieved from simple breast-region segmentation method if the input image has sufficient contrast with good interpretation of local details. We tested proposed method for MIAS mammogram images. To evaluate effectiveness of proposed method we choose three widely used metrics absolute mean brightness error, structural similarity index measure and peak signal to noise ratio for all 322 images of MIAS mammogram images database.

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References

  • Application programming interfae. https://en.wikipedia.org/wiki/Application_programming_interface.

  • Araujo, J. M. R. T. A. F., & Constantinou, C. E. (2014). New artificial life model for image enhancement. Expert Systems with Applications, 41, 5892–5906. doi:10.1016/j.eswa.2014.03.029.

    Article  Google Scholar 

  • Bartella, L., Smith, C. S., Dershaw, D. D., & Liberman, L. (2007). Imaging breast cancer. Radiologic Clinics North America, 45, 45–67. doi:10.1016/j.rcl.2006.10.007.

  • Beghdadi, A., & Negrate, A. (1989). Contrast enhancement technique based on local detection of edges. Computer Vision Graphics Image Processing, 46, 162–174.

    Article  Google Scholar 

  • Chan, B. S. K. L. H., Lo, S. B., & Helvie, M. (1995). Computer-aided detection of mammographic microcalcifications: Pattern recognition with artificial neural network. Medical Physics, 22(10), 1555–1567. doi:10.1118/1.597428.

    Article  Google Scholar 

  • Chen, W. S. S., & Zhang, W. (2013). An efficient universal noise removal algorithm combining spatial gradient and impulse statistic. Hindawi Publishing Corporation Mathematical Problems in Engineering, 2013, 1–12. doi:10.1155/2013/480274.

    MATH  Google Scholar 

  • Chu, K. (1999). An introduction to sensitivity, specificity, predictive values and likeli-hood ratios. Emergency Medicine Australasia, 11, 175–181. doi:10.1046/j.1442-2026.1999.00041.x.

  • Deng, J. (1989). The theory of a general quantum system interacting with a linear dissipative system. The Journal of Grey System, 1, 1–24. doi:10.1007/978-3-642-16158-2_1.

    MathSciNet  Google Scholar 

  • Dhawan, G. B. A. P., & Gordon, R. (1986). Enhancement of mammographic features by optimal adaptive neighborhood image processing. IEEE Transaction on Medical Imaging, 5, 8–15. doi:10.1109/TMI.1986.4307733.

    Article  Google Scholar 

  • Dirac, P. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9, 62–66. doi:10.1109/TSMC.1979.4310076.

    Article  Google Scholar 

  • Essentials of the java programming language. (2015). https://www.oracle.com/technetwork/java/index-138747.html.

  • Gang, L. (2009). Image local contrast enhancement based on grey relational analysis. In International Symposium on Computer Network and Multimedia Technology (pp. 1–4). doi:10.1109/CNMT.2009.5374554.

  • Gordon, R., & Rangayyan, R. (1984). Feature enhancement of film mammograms using fixed and adaptive neighborhoods. Applied Optics, 19(1–12), 560–564. doi:10.1364/AO.23.000560.

    Article  Google Scholar 

  • Hamarneh, D. N. G., & Adler, A. (2010). A new preprocessing filter for digital mammograms. Lecture Notes in Computer Science, 61, 585–592. doi:10.1007/978-3-642-13681-8_68.

  • Hassanpour, S. S. H., & Samadiani, N. (2015). Using morphological transforms to enhance the contrast of medical images. The Egyptian Journal of Radiology and Nuclear Medicine, 46, 481–489. doi:10.1016/j.ejrnm.2015.01.004.

    Article  Google Scholar 

  • Hua, H. F. L. Zhou, Z., Ding, L. (2012) A new color medical image enhancement method. In IET International Conference on Information Science and Control Engineering (pp. 3–6). doi:10.1049/cp.2012.2465.

  • Jaya V. L., & Gopikakumari, R. (2015). Fuzzy rule based enhancement in the smrt domain for low contrast images. In International Conference on Information and Communication Technologies 46 (pp. 1747–1753). doi:10.1016/j.procs.2015.02.125.

  • Kallergi, L. C. M., & Qian, W. (1996). Interpretation of calcifications in screen/film, digitized and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study. Academic Radiology, 3, 285–293. doi:10.1016/S1076-6332(96)80240-6.

    Article  Google Scholar 

  • Kim, M., & Chung, G. (2008). Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Transactions on Consumer Electronics, 54, 1389–1397. doi:10.1109/TCE.2008.4637632.

    Article  Google Scholar 

  • Kim, K. S. J. K., Park, J. M., & Park, H. (1997). Adaptive mammographic image enhancement using first derivative and local statistics. IEEE Transaction Medical Imaging, 16, 495–502. doi:10.1109/42.640739.

    Article  Google Scholar 

  • Kom, A. T. G., & Kom, M. (2007). Automated detection of masses in mammograms by local adaptive thresholding. Computers in Biology and Medicine, 37, 118–173. doi:10.1016/j.compbiomed.2005.12.004.

    Article  Google Scholar 

  • Maa, Z., Manuel, J., Tavaresa, R. S., & Natal, R. (2009). A review on the current segmentation algorithms for medical images. In 1st International Conference on Imaging Theory and Applications (IMAGAPP) (pp. 135–140). doi:10.5220/0001793501350140.

  • Maa, Z., Manuel, J., Tavaresa, R. S., & Natal, R. (2010). A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Computer Methods in Biomechanics and Biomedical Engineering (pp. 235–246). doi:10.1080/10255840903131878.

  • Manuel, R. T. J. (2010). Image processing and analysis: Applications and trends. In AES-ATEMA 2010 5th International Conference on Advances and Trends in Engineering Materials and their Applications (pp. 27–41).

  • Matlab function adapthisteq() specification. https://www.mathworks.in/help/images/ref/adapthisteq.html.

  • Min, L. Z. W. L. C. Z. L. Y. Q., Shao, K., & Yang, J. (2015). Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging. World Journal of Surgical Oncology, 24, 1–7. doi:10.1186/s12957-014-0431-3.

    Article  Google Scholar 

  • Mohan, S., & Ravishankar, M. (2013). Modified contrast limited adaptive histogram equalization based on local contrast enhancement for mammogram images. Mobile Communication and Power Engineering Communications in Computer, 296, 397–403. doi:10.1007/978-3-642-35864-7_60.

    Google Scholar 

  • Najdawi, N. A., Biltawi, M., & Tedmori, S. (2015). Mammogram image visual enhancement, mass segmentation and classification. Applied Soft Computing, 35, 175–185. doi:10.1016/j.asoc.2015.06.029.

    Article  Google Scholar 

  • Ojala, O. N. T., & Nppi, J. (2001). Accurate segmentation of the breast region from digitized mammograms. Medical Imaging Graph, 25, 47–59. doi:10.1016/S0895-6111(00)00036-7.

    Article  Google Scholar 

  • Package javax.imageio. http://docs.oracle.com/javase/7/docs/api/javax/imageio/package-summary.html.

  • Pizer, E. A. S. M., & Austin, J. (1987). Adaptive histogram equalization and its variations. Physica, 19(1–12), 355–368. doi:10.1016/S0734-189X(87)80186-X.

    Google Scholar 

  • Polesel, G. R. A., & Mathews, V. (2000). Image enhancement via adaptive unsharp masking. IEEE Transaction Image Processing, 9(9), 505–510. doi:10.1109/83.826787.

    Article  Google Scholar 

  • Raba, D., Oliver, A., Marti, J., Peracaula, M., & Espunya, J. (2005). Breast segmentation with pectoral muscle suppression on digital mammograms. Pattern Recognition and Image Analysis, 3523, 471–478. doi:10.1007/11492542_58.

  • Rangayyan, L. S. R. M., & Shen, Y. (1997). Improvement of sensitivity of breast cancer diagnosis with adaptiveneighborhood contrast enhancement of mammograms. IEEE Transaction Information Technology Biomedicine, 1, 161–170. doi:10.1109/4233.654859.

    Article  Google Scholar 

  • Saleem, A. B. A., & Boashash, B. (2012). Image fusion-based contrast enhancement. EURASIP Journal on Image and Video Processing, 10, 1–17. doi:10.1186/1687-5281-2012-10.

    Google Scholar 

  • Sampaioa, W. B. (2011). Detection of masses in mammogram images using cnn. Geostatistic functions and svm. Computers in Biology and Medicine, 41, 653–664. doi:10.1016/j.compbiomed.2011.05.017.

  • Schiabel, H., Santos, V. T., & Angelo, M. F. (2008). Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography cad schemes. In The 23rd Annual ACM Symposium on Applied Computing (pp. 1333–1337). doi:10.1145/1363686.1363996.

  • Screening for breast cancer with mammography. www.cochrane.dk/screening/mammography-leaflet.pdf.

  • Serra, J. (1983). Image Analysis and Mathematical Morphology. Orlando, FL: Academic Press Inc.

    Google Scholar 

  • Shahedi, M. B. K. F. A. S. S., & Amirfattahi, R. (2007). Accurate breast region detection in digital mammograms using a local adaptive thresholding method. In International workshop on image analysis for multimedia interactive services (pp. 26–30). doi:10.1109/WIAMIS.2007.15.

  • Sim, C. T. K. S., & Tan, Y. (2007). Recursive sub-image histogram equalization applied to gray scale images. Annals of Physics, 28, 1209–1221. doi:10.1016/j.patrec.2007.02.003.

    Google Scholar 

  • Suckling, J. (1994). The mammographic image analysis society digital mammogram database. Exerpta Medica International Congress Series, 19, 375–378.

    Google Scholar 

  • Sundaram, N. A. M., Ramar, K., Prabin, G. (2011). Histogram based contrast enhancement for mammogram images. In International Conference on Signal Processing, Communication, Computing and Networking Technologies (pp. 842–846). doi:10.1109/ICSCCN.2011.6024667.

  • Sundaram, N. A. M., Ramar, K., & Prabin, G. (2011). Histogram modified local contrast enhancement for mammogram images. Applied Soft Computing, 11, 5809–5816. doi:10.1016/j.asoc.2011.05.003.

    Article  Google Scholar 

  • Tiwari, M., Gupta, B., & Shrivastava, M. (2014). High-speed quantile-based histogram equalisation for brightness preservation and contrast enhancement. IET Image Processing, 9, 80–89. doi:10.1049/iet-ipr.2013.0778.

    Article  Google Scholar 

  • Wang, H. S. Z., Bovik, A. C., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600–612. doi:10.1109/TIP.2003.819861.

    Article  Google Scholar 

  • Wang, P. F. W. Z. K., & Qin, H. (2006). Automatic registration of mammograms using texture-based anisotropic features. Proceedings of the IEEE International Symposium on Biomedical Imaging, 24, 864–867. doi:10.1109/ISBI.2006.1625055.

    Google Scholar 

  • Yang, Z. S. Y., & Sun, L. (2010). Medical image enhancement algorithm based on wavelet transform. Electronics Letters, 46(2), 120–121. doi:10.1049/el.2010.2063.

    Article  Google Scholar 

  • Yeong, T. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43, 1–8. doi:10.1109/30.580378.

    Article  Google Scholar 

  • Zuiderveld, K. (1994). Graphics gems (Vol. IV). MA: AddisonWesley.

    Google Scholar 

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the work. They also thank Mammography Image Analysis Society for providing free access of MIAS mammogram images.

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Correspondence to Bhupendra Gupta.

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Gupta, B., Tiwari, M. A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis. Multidim Syst Sign Process 28, 1549–1567 (2017). https://doi.org/10.1007/s11045-016-0432-1

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