Automated detection of breast cancer in thermal infrared images, based on independent component analysis

Journal of Medical Systems

Volumn 36, Issue 1, 2012, Pages 103-111

  Boquete, Luciano ^a   Ortega, Sergio ^a   Miguel Jiménez, Juan Manuel ^a   Rodríguez Ascariz, José Manuel ^a   Blanco, Román ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Breast cancer; ICA; Image processing; Thermography

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATION; BREAST CANCER; CANCER DIAGNOSIS; CANCER LOCALIZATION; CLINICAL ARTICLE; CONTROLLED STUDY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; INDEPENDENT COMPONENT ANALYSIS; INFRARED PHOTOGRAPHY; MASTITIS; SENSITIVITY AND SPECIFICITY; BREAST TUMOR; FEMALE; INFRARED RADIATION; PRINCIPAL COMPONENT ANALYSIS; THERMOGRAPHY;

BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFRARED RAYS; PRINCIPAL COMPONENT ANALYSIS; THERMOGRAPHY;

EID: 84860243246     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9450-y     Document Type: Article

References (34)

1. Ferlay, J., Bray, F., Pisani, P., and Parkin, D. M., Globocan 2002: Cancer incidence, mortality and prevalence worldwide, IARC CancerBase no.5, ver. 2.0. Lyon: IARC, 2004.
2. Boyd, B. A., and Fine, R. E., Stereotactic breast biopsy: the nurse's role. J. Radiol. Nurs. 26:4-10, 2007. doi:10.1016/j. jradnu.2006.11.001. (Pubitemid 46275948)
3. Gautherine, M., Thermopathology of breast cancer: measurement and analysis of in vivo temperature and blood flow. Ann. NY Acad. Sci. 1980:383-415, 1999.
4. 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 Appl. 33:652-666, 2007. (Pubitemid 46389481)
5. Zou, Y., and Guo, Z., A review of electrical impedance techniques for breast cancer detection. Med. Eng. Phys. 25:79-90, 2003. (Pubitemid 36109168)
6. Mandelblatt, J. S., et al., Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann. Intern. Med. 151:738-747, 2009.
7. Ng, E. Y. K., A review of thermography as promising noninvasive detection modality for breast tumor. Int. J. Therm. Sci. 48:849-859, 2009. doi:10.1016/j.ijthermalsci.2008.06.015.
8. Zhixiong, G., and Kan-Wan, S., Simulated parametric studies in optical imaging of tumors through temporal log-slope difference mapping. Med. Eng. Phys. 29:1142-1148, 2007.
9. Tan, J. M. Y., Ng, E. Y. K., Acharya, R., 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. doi:10.1007/s10916-008-9174-4.
10. Orel, V. E., Romanov, A. V., Dzyatkovskaya, N. N., and Mel'nik, Y. I., The device and algorithm for estimation of the mechanoemisson chaos in blood of patients with gastric cancer. Med. Eng. Phys. 24:365-371, 2002. (Pubitemid 34607445)
11. Yahara, T., Koga, T., Yoshida, S., Nakagawa, S., Deguchi, H., and Shirouzu, K., Relationship between microvessel density and thermographic hot areas in breast cancer. Surg. Today. 33:243-248, 2003. doi:10.1007/ s005950300055. (Pubitemid 36549578)
12. Carmeliet, P., and Jain, R. K., Angiogenesis in cancer and other diseases. Nature. 407:249-57, 2000.
13. Lloyd-Williams, K., and Handley, R. S., Infrared thermometry in the diagnosis of breast disease. Lancet. 2:1378-1381, 1961.
14. Parisky, Y. R., Sardi, A., Hamm, R., Hughes, K., Esserman, L., Rust, S., and Callahan, K., Efficacy of computerized infrared imaging analysis to evaluate mammographically suspicious lesions. AJR. 180:263-269, 2003. (Pubitemid 36008723)
15. Arora, N., Martins, D., Ruggerio, D., Tousimis, E., Swistel, A. J., Osborne, M. P., and Simmons, R. M., Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am. J. Surg. 196:523-526, 2008.
16. Kennedy, D. A., A comparative review of thermography as a breast cancer screening technique. Integr. Cancer Ther. 8:9-16, 2009. doi:10.1177/ 1534735408326171.
17. Qi, H., Kurungati, P. T., and Liu, Z., Early detection of breast cancer using thermal texture maps. Biomedical imaging 2002. Proceedings. 2002 IEEE International Symposium on. doi:10.1109/ISBI.2002.1029255.
18. Tang, X., and Ding H., Asymmetry analysis of breast thermograms with morphological image segmentation. Eng in Med and Bio Soc 2005, 27th Annual conference, IEEE-EMBS 2005, 10.1109/IEMBS.2005.1616766
19. Qi, H., and Head, J. F., Asymmetry analysis using automatic segmentation and classification for breast cancer detection in thermograms. 23rd IEEE International Conference on Eng in Med and Bio 2001.
20. Tang, X., Ding, H., Yuan, Y., and Wang, Q., Morphological measurement of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. Biomed. Signal Process. Contr. 3:312-318, 2008.
21. Ng, E. Y. K., and Kee, E. C., Integrative computer-aided diagnostic with breast thermogram. J.F Mechanics in Medicine and Biology 7:1-10, 2007. doi:10.1142/so219519407002091.
22. Koay, J., Herry, C., and Frize, M., Analysis of breast thermography with an artificial neural network. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA September 2004;1-5.
23. EtehadTavakol, M., Sadri, S., and Ng, E. Y. K., Application of kand fuzzy c-means for color segmentation of thermal infrared breast images. J. Med. Syst., 2008. doi:10.1007/s10916-008-9213-1.
24. Schaefer, G., Nakashima, T., Zaivisek, M., Yokota, Y., Drastich, A., and Ishibuchi, H., Breast cancer classification using statistical features and fuzzy classification of thermograms. Fuzzy Systems Conference 2007, FUZZ-IEEE 2007, IEEE International, doi:10.1109/FUZZY.2007.4295520.
25. Schaefer, G., Závišek, M., and Nakashima, T., Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 42:1133-1137, 2009. doi:10.1016/j.patcog.2008. 08.007.
26. Zhao, Q., Zhang, J., Wang, R., and Cong, W., Use of a thermocouple for malignant tumor detection. IEEE Eng. Med. Biol. Mag. 27:164-66, 2008.
27. Jakubowska, T., Wiecek, B., Wysocki, M., and Drews-Peszynski, C., Thermal signatures for breast cancer screening comparative study. In Proceedings of the 25th Annual International Conference of the IEEE EMBS Conference, Cancun. 2:1117-1120, 2003.
28. Ng, E.Y. K., and Fok, S. C., A framework for early discovery of breast tumor using thermography with artificial neural network. Breast J. 9:4341-343, 2003. doi:10.1046/j.1524-4741.2003.09425.x. (Pubitemid 36927425)
29. http://aathermography.com (last accessed Nov 2009).
30. Bronzino, J. D. (Ed.), Medical Devices and Systems (Biomedical Engineering Handbook), Ed. J. D. Bronzino, Publ. Taylor & Francis, pp. 25.1-25.20, 2006.
31. Abu-Amara, F., and Abdel-Qader, I., Detection of breast cancer using independent component analysis. Electro/Information Technology, 2007 IEEE International Conference on, pp. 428-431. doi:10.1109/EIT.2007.4374509.
32. Gallardo-Caballero, R., García-Orellana, C. J., Macías-Macías, M., González-Velasco, H. M., and López-Aligué, F. J., Independent component analysis applied to breast cancer detection on digitized mammograms. Int. Congr. Ser. 1281:1052-1057, 2005. doi:10.1016/j.ics.2005.03.072. (Pubitemid 43693632)
33. Amari, S., Chen, T., and Cichocki, A., Non-holonomic constraints in learning blind source separation. Kasabov, N. (Eds.), Progress in Connectionist-Based Information Systems, ICONIP-97, Vol. I. New Zealand: Springer, pp. 633-636, 1997.
34. Otsu, N., A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9:62-66, 1979. (Pubitemid 9413341)