A new approach for breast abnormality detection based on thermography
DOI:
https://doi.org/10.26415/2572-004X-vol2iss3p245-254Keywords:
Breast cancer · Thermography · Image processing · SVM · CAD systemAbstract
Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.
Downloads
Download data is not yet available.
References
[1] Ibrahim, Abdelhameed, Shaimaa Mohammed, and Hesham Arafat Ali. "Breast Cancer Detection and Classification Using Thermography: A Review." International Conference on Advanced Machine Learning Technologies and Applications. Springer, Cham, (2018).
[2] Hamdi-Cherif, M., et al. "Cancer estimation of incidence and survival in Algeria 2014." J Cancer Res Ther 3.9 (2015): 100-104.
[3] Lanisa, Norlailah, Ng Siew Cheok, and Lai Khin Wee. "Color morphology and segmentation of the breast thermography image." Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on. IEEE, (2014).
[4] Francis, Sheeja V., M. Sasikala, and S. Saranya. "Detection of breast abnormality from thermograms using curvelet transform based feature extraction." Journal of medical systems 38.4 (2014): 23.
[5] Lahiri, B. B., et al. "Medical applications of infrared thermography: a review." Infrared Physics & Technology 55.4 (2012): 221-235.
[6] Etehadtavakol, Mahnaz, and Eddie YK Ng. "An Overview of Medical Infrared Imaging in Breast Abnormalities Detection." Application of Infrared to Biomedical Sciences. Springer, Singapore, (2017). 45-57.
[7] Yaneli, Ameca-Alducin Maria, et al. "Assessment of bayesian network classifiers as tools for discriminating breast cancer pre-diagnosis based on three diagnostic methods." Mexican International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, (2012).
[8] Gautherie, Michel, and Charles M. Gros. "Breast thermography and cancer risk prediction." Cancer 45.1 (1980): 51-56.
[9] Kandlikar, Satish G., et al. "Infrared imaging technology for breast cancer detection–Current status, protocols and new directions." International Journal of Heat and Mass Transfer 108. USA (2017): 2303-2320.
[10] Vardasca, Ricardo, Lucia Vaz, and Joaquim Mendes. "Classification and Decision Making of Medical Infrared Thermal Images." Classification in BioApps. Springer, Cham, (2018). 79-104.
[11] Acharya, U. Rajendra, et al. "Thermography based breast cancer detection using texture features and support vector machine." Journal of medical systems 36.3 (2012): 1503-1510.
[12] Schaefer, Gerald, Michal Závišek, and Tomoharu Nakashima. "Thermography based breast cancer analysis using statistical features and fuzzy classification." Pattern Recognition 42.6 (2009): 1133-1137.
[13] Pramanik, Sourav, Debotosh Bhattacharjee, and Mita Nasipuri. "Wavelet based thermogram analysis for breast cancer detection." Advanced Computing and Communication (ISACC), 2015 International Symposium on. IEEE, (2015).
[14] Acharya, U. Rajendra, et al. "Higher order spectra analysis of breast thermograms for the automated identification of breast cancer." Expert Systems 31.1 (2014): 37-47.
[15] Lee, Ming-Yih, and Chi-Shih Yang. "Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images." Computer methods and programs in biomedicine 100.3 (2010): 269-282.
[16] Tan, Tuan Zea, et al. "A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure." Expert Systems with Applications 33.3 (2007): 652-666.
[17] Gerald, S. "ACO classification of thermogram symmetry features for breast cancer diagnosis". Memetic Computing, (2014). 6(3): p. 207-212. Springer
[18] Silva, L. F., et al. "A new database for breast research with infrared image." Journal of Medical Imaging and Health Informatics 4.1 (2014): 92-100.
[19] Borchartt, Tiago B., et al. "Breast thermography from an image processing viewpoint: A survey." Signal Processing 93.10 (2013): 2785-2803.
[20] Suganthi, S. S., and S. Ramakrishnan. "Semi automatic segmentation of breast thermograms using variational level set method." The 15th International Conference on Biomedical Engineering. Springer, Cham, (2014).
[21] Golestani, N., M. EtehadTavakol, and E. Y. K. Ng. "Level set method for segmentation of infrared breast thermograms." EXCLI journal 13 (2014): 241.
[22] Srinivasan, Suganthi Salem, and Ramakrishnan Swaminathan. "Segmentation of breast tissues in infrared images using modified phase based level sets." Biomedical Informatics and Technology. Springer, Berlin, Heidelberg, (2014). 161-174.
[23] De Oliveira, J. P. S., et al. "Segmentation of infrared images: A new technology for early detection of breast diseases." Industrial Technology (ICIT), 2015 IEEE International Conference on. IEEE, (2015).
[24] Shang, Zhigang, and Mengmeng Li. "Combined feature extraction and selection in texture analysis." Computational Intelligence and Design (ISCID), 2016 9th International Symposium on. Vol. 1. IEEE, (2016).
[25] Haralick, Robert M., and Karthikeyan Shanmugam. "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
[26] Milosevic, Marina, Dragan Jankovic, and Aleksandar Peulic. "Thermography based breast cancer detection using texture features and minimum variance quantization." EXCLI journal 13 (2014): 1204.
[27] Rassiwala, Muffazzal, et al. "Evaluation of digital infra–red thermal imaging as an adjunctive screening method for breast carcinoma: A pilot study." International Journal of Surgery 12.12 (2014): 1439-1443.
[2] Hamdi-Cherif, M., et al. "Cancer estimation of incidence and survival in Algeria 2014." J Cancer Res Ther 3.9 (2015): 100-104.
[3] Lanisa, Norlailah, Ng Siew Cheok, and Lai Khin Wee. "Color morphology and segmentation of the breast thermography image." Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on. IEEE, (2014).
[4] Francis, Sheeja V., M. Sasikala, and S. Saranya. "Detection of breast abnormality from thermograms using curvelet transform based feature extraction." Journal of medical systems 38.4 (2014): 23.
[5] Lahiri, B. B., et al. "Medical applications of infrared thermography: a review." Infrared Physics & Technology 55.4 (2012): 221-235.
[6] Etehadtavakol, Mahnaz, and Eddie YK Ng. "An Overview of Medical Infrared Imaging in Breast Abnormalities Detection." Application of Infrared to Biomedical Sciences. Springer, Singapore, (2017). 45-57.
[7] Yaneli, Ameca-Alducin Maria, et al. "Assessment of bayesian network classifiers as tools for discriminating breast cancer pre-diagnosis based on three diagnostic methods." Mexican International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, (2012).
[8] Gautherie, Michel, and Charles M. Gros. "Breast thermography and cancer risk prediction." Cancer 45.1 (1980): 51-56.
[9] Kandlikar, Satish G., et al. "Infrared imaging technology for breast cancer detection–Current status, protocols and new directions." International Journal of Heat and Mass Transfer 108. USA (2017): 2303-2320.
[10] Vardasca, Ricardo, Lucia Vaz, and Joaquim Mendes. "Classification and Decision Making of Medical Infrared Thermal Images." Classification in BioApps. Springer, Cham, (2018). 79-104.
[11] Acharya, U. Rajendra, et al. "Thermography based breast cancer detection using texture features and support vector machine." Journal of medical systems 36.3 (2012): 1503-1510.
[12] Schaefer, Gerald, Michal Závišek, and Tomoharu Nakashima. "Thermography based breast cancer analysis using statistical features and fuzzy classification." Pattern Recognition 42.6 (2009): 1133-1137.
[13] Pramanik, Sourav, Debotosh Bhattacharjee, and Mita Nasipuri. "Wavelet based thermogram analysis for breast cancer detection." Advanced Computing and Communication (ISACC), 2015 International Symposium on. IEEE, (2015).
[14] Acharya, U. Rajendra, et al. "Higher order spectra analysis of breast thermograms for the automated identification of breast cancer." Expert Systems 31.1 (2014): 37-47.
[15] Lee, Ming-Yih, and Chi-Shih Yang. "Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images." Computer methods and programs in biomedicine 100.3 (2010): 269-282.
[16] Tan, Tuan Zea, et al. "A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure." Expert Systems with Applications 33.3 (2007): 652-666.
[17] Gerald, S. "ACO classification of thermogram symmetry features for breast cancer diagnosis". Memetic Computing, (2014). 6(3): p. 207-212. Springer
[18] Silva, L. F., et al. "A new database for breast research with infrared image." Journal of Medical Imaging and Health Informatics 4.1 (2014): 92-100.
[19] Borchartt, Tiago B., et al. "Breast thermography from an image processing viewpoint: A survey." Signal Processing 93.10 (2013): 2785-2803.
[20] Suganthi, S. S., and S. Ramakrishnan. "Semi automatic segmentation of breast thermograms using variational level set method." The 15th International Conference on Biomedical Engineering. Springer, Cham, (2014).
[21] Golestani, N., M. EtehadTavakol, and E. Y. K. Ng. "Level set method for segmentation of infrared breast thermograms." EXCLI journal 13 (2014): 241.
[22] Srinivasan, Suganthi Salem, and Ramakrishnan Swaminathan. "Segmentation of breast tissues in infrared images using modified phase based level sets." Biomedical Informatics and Technology. Springer, Berlin, Heidelberg, (2014). 161-174.
[23] De Oliveira, J. P. S., et al. "Segmentation of infrared images: A new technology for early detection of breast diseases." Industrial Technology (ICIT), 2015 IEEE International Conference on. IEEE, (2015).
[24] Shang, Zhigang, and Mengmeng Li. "Combined feature extraction and selection in texture analysis." Computational Intelligence and Design (ISCID), 2016 9th International Symposium on. Vol. 1. IEEE, (2016).
[25] Haralick, Robert M., and Karthikeyan Shanmugam. "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
[26] Milosevic, Marina, Dragan Jankovic, and Aleksandar Peulic. "Thermography based breast cancer detection using texture features and minimum variance quantization." EXCLI journal 13 (2014): 1204.
[27] Rassiwala, Muffazzal, et al. "Evaluation of digital infra–red thermal imaging as an adjunctive screening method for breast carcinoma: A pilot study." International Journal of Surgery 12.12 (2014): 1439-1443.
Published
2018-09-30
Issue
Section
Medical technologies
