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Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

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Abstract

Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.

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References

  1. Ahmad, Z., Khurshid, A., Qureshi, A., Idress, R., Asghar, N., and Kayani, N., Breast carcinoma grading, estimation of tumor size, axillary lymph node status, staging, and nottingham prognostic index scoring on mastectomy specimens. Indian J. Pathol. Microbiol. 52:477–481, 2009.

    Article  Google Scholar 

  2. http://www.who.int/cancer/detection/breastcancer/en/index1.html (Last accessed Aug 2010).

  3. http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/ (Last accessed on Aug 2010).

  4. Coleman, M. P., Quaresma, M., Berrino, F., Lutz, J. M., De Angelis, R., Capocaccia, R., Baili, P., Rachet, B., Gatta, G., Hakulinen, T., Micheli, A., Sant, M., Weir, H. K., Elwood, J. M., Tsukuma, H., Koifman, S., E Silva, G. A., Francisci, S., Santaquilani, M., Verdecchia, A., Storm, H. H., and Young, J. L., Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol. 9:730–756, 2008.

  5. Tan, J. M., Ng, E. Y. K., Acharya, R. U., Keith, L. G., and Holmes, J., Comparative study on the use of analytical software to identify the different stages of breast cancer using discrete temperature data. J. Med. Syst. 33:141–153, 2009.

    Article  Google Scholar 

  6. Keyserlingk, J. R., Ahlgren, P. D., Yu, E., Belliveau, N., and Yassa, M., Functional Infrared Imaging of the breast. IEEE Eng. Med. Biol. 19:30–42, 2000.

    Article  Google Scholar 

  7. Elmore, J. G., Wells, C. F., and Carol, M. P. H., Variability in radiologists interpretation of mammograms. N. Engl. J. Med. 331:99–104, 1993.

    Google Scholar 

  8. Wright, H., Listinsky, J., Rim, A., Chellman-Jeffers, M., Patrick, R., Rybicki, L., Kim, J., and Crowe, J., Magnetic resonance imaging as a diagnostic tool for breast cancer in premenopausal women. Am J Surg. 190:572–575, 2005.

    Article  Google Scholar 

  9. Elmore, J. G., Armstrong, K., Lehman, C. D., and Fletcher, S. W., Screening for breast cancer. JAMA 293:1245–1256, 2005.

    Article  Google Scholar 

  10. Gautherine, M., and Gros, C., Contribution of infrared thermography to early diagnosis, pretheraputic prognosis and post-irradiation follow-up of breast carcinomas. Med. Mundi. 21:135–149, 1976.

    Google Scholar 

  11. Gros, C., Gautherine, M., and Bourjat, P., Prognosis and post therapeutic follow-up of breast cancers by thermography. Bibl. Radiol. 6:77–90, 1975.

    Google Scholar 

  12. Fok, S. C., Ng, E. Y. K., and Tai, K., Early detection and visualization of breast tumor with thermogram and neural network. J. Mech. Med. Biol. 2:185–195, 2002.

    Article  Google Scholar 

  13. Gautherie, M., and Gros, C. M., Breast thermography and cancer risk prediction. Cancer. 45:51–56, 1980.

    Article  Google Scholar 

  14. Louis, K., Walter, J., and Gautherie, M., Long-term assessment of breast cancer risk by thermal imaging. Prog. Clin. Biol. Res. 107:279–301, 1982.

    Google Scholar 

  15. Amalric, R., Giraud, D., Altschuler, C., Amalric, F., Spitalier, J. M., Brandone, H., Ayme, Y., and Gardiol, A. A., Does infrared thermography truly have a role in present-day breast cancer management? Prog. Clin. Biol. Res. 107:269–78, 1982.

    Google Scholar 

  16. Ng, E. Y. K., Chen, Y., and Ung, L. N., Computerized breast thermography: Study of Image segmentation and temperature cyclic variations. Intl. J. Med. Eng. Technol. 25:12–16, 2001.

    Article  Google Scholar 

  17. Jakubowska, T., Wiecek, B., Wysocki, M., and Drews-Peszynski, C., Thermal Signatures for Breast Cancer Screening - Comparative Study. Proc. IEEE EMBS Conf. Cancun, Mexico, 2003.

  18. Ng, E. Y. K., and Sudharsan, N. M., Numerical modelling in conjunction with thermography as an adjunct tool for breast tumour detection. BMC Cancer 4:1–26, 2004.

    Article  Google Scholar 

  19. Ng, E. Y. K., A review of thermography as promising non-invasive detection modality for breast tumour. Intl. J. Thermal Sci. 48:849–859, 2009.

    Article  Google Scholar 

  20. Tuceryan, M., and Jain, A. K., Texture analysis. in: C.H. Chen, L.F. Pau, and P.S.P. Wang, (Eds.), Handbook of pattern recognition & computer vision, 1993.

  21. Tan, J. H., Ng, E. Y. K., and Acharya, U. R., Study of normal ocular thermogram using textural parameters. Infrared Phys. Techn. 53:120–126, 2010.

    Article  Google Scholar 

  22. Ng, E. Y. K., Chen, Y., Ung, L. N., Fok, S. K., and Wan, I. S. Y., Thermography as an Indicator of Breast Blood Perfusion. Proc. 10th Inl. Conf. on Biomed. Eng., Singapore Ed: JCH Goh, Humanities Press 275–276, 2000.

  23. Ng, E. Y. K., Ung, L. N., Ng, F. C., and Sim, L. S. J., Statistical analysis of healthy and malignant breast thermography. J. Med. Eng. Technol. 25:253–263, 2001.

    Article  Google Scholar 

  24. Thermography Guidelines (TG), Standards and Protocols in Clinical Thermographic Imaging, http://www.iact-org.org/professionals/thermog-guidelines.html, 2002 (Last accessed Aug 2010).

  25. Amalu, W. C., Hobbins, W. B., Head, J. F., and Elliott, R. L., Infrared imaging of the breast—an overview. In Biomedical Engineering Handbook, CRC Press, chapter 25-1 to 25–21, 2006.

  26. Ammer, K., and Ring, E. F. L., Standard procedures for infrared imaging in medicine. In Biomedical Engineering Handbook, CRC Press, chapter 36-1 to 36–14, 2006.

  27. Qi, H., Kuruganti, P. T., and Snyder, W. E., Detecting breast cancer from thermal infrared images by asymmetry analysis. In Biomedical Engineering Handbook, CRC Press, ch. 27-1 to 27–14, 2006.

  28. Ring, E. F. J., and Ammer, K., The technique of infra red imaging in medicine. Thermology Intl. 10:7–14, 2000.

    Google Scholar 

  29. Jung, A., and Zuber, J., Thermographic methods in medical diagnostics. Med, Warsaw, 1998.

    Google Scholar 

  30. Head, J. F., Lipari, C. A., Wang F., and Elliot, R. L., Image analysis of digitized infrared images of the breasts from a first generation infrared imaging system. Proc 19th Intl. Conf. IEEE/EMBS Chicago, IL. USA, 1997.

  31. Ng, E. Y. K., and Chen, Y., Segmentation of breast thermogram: Improved boundary detection with modified snake algorithm. J. Mech. Med. Biol. 6:123–136, 2006.

    Article  Google Scholar 

  32. Gonzalez, R. C., and Woods, R. E., Digital image processing, 2nd edition. Prentice Hall, New Jersey, 2001.

    Google Scholar 

  33. Tomita, F., and Tsuji, S., Computer analysis of visual textures. Kluwer Academic, Boston, 1990.

    MATH  Google Scholar 

  34. Weszka, J. S., and Rosenfield, A., An application of texture analysis to material inspection. Pattern Recogn. 8:195–200, 1976.

    Article  Google Scholar 

  35. Ramana, K. V., and Ramamoorthy, B., Statistical methods to compare the texture features of machined surfaces. Pattern Recogn. 29:1447–1459, 1996.

    Article  Google Scholar 

  36. Galloway, M. M., Texture analysis using gray level run length. Comput. Graph. Image Proc. 4:172–179, 1975.

    Article  Google Scholar 

  37. Brekelmans, C. T. M., Westers, P., Faber, J. A. J., Peeters, P. H. M., and Collette, H. J. A., Age specific sensitivity and sojourn time in a breast cancer screening programme (DOM) in The Netherlands: a comparison of different methods. J. Epidemiol. Community Health 50:68–71, 1996.

    Article  Google Scholar 

  38. Schaefer, G., Závišek, M., and Nakashima, T., Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 42:1133–1137, 2009.

    Article  Google Scholar 

  39. Tan, T. Z., Quek, C., Ng, G. S., and Ng, E. Y. K., A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure expert systems with applications. Expert Syst. Appl. 33:652–666, 2007.

    Article  Google Scholar 

  40. EtehadTavakol, M., Sadri, S., and Ng, E. Y. K., Application of k- and fuzzy C-means for color segmentation of thermal infrared breast images. J. Med. Syst. 34:35–42, 2010.

    Article  Google Scholar 

  41. Wiecek, B., Wiecek, M., Strakowski, R., Jakubowska, T., and Ng, E.Y.K., Wavelet-based thermal image classification for breast screening and other medical applications, chp. 12, Eds E. Y. K. Ng, U. R. Acharya, and J. S. Suri, Performance evaluation techniques in multi-modality breast cancer screening, Diagnosis and Treatment, American Scientific Publishers, 2010.

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Acknowledgement

Authors thank Goh Yan Kun and Abdul Mutalib Bin Abdul Hamid for helping in running the codes, and acknowledge and thank Dr. Llewellyn Sim, Senior Consultant, Department of Diagnostic Radiology, Singapore General Hospital, Singapore, for providing the thermogram images.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 535, Clementi Road, Singapore, 599489, Singapore

    U. Rajendra Acharya & Jen-Hong Tan

  2. School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore, 639798, Singapore

    E. Y. K. Ng, Jen-Hong Tan & S. Vinitha Sree

Authors
  1. U. Rajendra Acharya
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  2. E. Y. K. Ng
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  3. Jen-Hong Tan
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  4. S. Vinitha Sree
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Correspondence to S. Vinitha Sree.

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Acharya, U.R., Ng, E.Y.K., Tan, JH. et al. Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine. J Med Syst 36, 1503–1510 (2012). https://doi.org/10.1007/s10916-010-9611-z

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  • DOI: https://doi.org/10.1007/s10916-010-9611-z

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