Uso de imagens termográficas para classificação de anormalidades de mama
Abstract
Recent studies involve the use of thermal imaging as a screening technique especially in cases where the mammography is limited. The aim of this work is to evaluate the feasibility of using thermographic images in order to detect breast cancer. A three-stage feature extraction approach is proposed. Initially, four intervals variables are obtained by the minimum and maximum temperature values from the morphological and thermal matrices. In the second step, operators based on dissimilarities for intervals are considered and then continuous features are obtained, giving the input data to a classification process. The approach achieved 16% of misclassification rate, 85.7% of sensitivity and 86.5% of specificity to the malignant class.
References
Acharya, U. R., Ng, E. Y. K., Tan, J.-H., and Sree, S. V. (2012). Thermography based breast cancer detection using texture features and support vector machine. Journal of Medical Systems, 36:1503–1510.
Bezerra, L., Oliveira, M. M., Rolim, T. L., Conci, A., Santos, F. G. S., Lyra, P. R. M., and Lima, R. C. F. (2013a). Estimation of breast tumor thermal properties using infrared images. Signal Processing, 93:2851–2863.
Bezerra, L. A., Oliveira, M. M., Araújo, M. C., Viana, M. J. A., Santos, L. C., Santos, F. G. S., Rolim, T. L., Lyra, P. R. M., Lima, R. C. F., Borschartt, T. B., Resmini, R., and Conci, A. (2013b). Infrared imaging for breast cancer detection with proper selection of properties: From acquisition protocol to numerical simulation. In Ng, E. Y. K., Acharya, U. R., Rangayyan, R. M., and Suri, J. S., editors, Multimodality Breast Imaging: Diagnosis and Treatment, volume 1. SPIE PRESS.
da Cunha Queiroz, K. F. F. (2014). Análise da repetitividade e melhoria de segmentação semiautomática de rois em imagens termográficas de mama. Trabalho de conclus˜ao de curso (Monografia) – Universidade Federal de Pernambuco, Recife, 2014.
de Souza, R. M. C. R. (1999). Classificação de imagens sar baseada em uma abordagem simbólica. Master’s thesis, Universidade Federal de Pernambuco, Recife.
Diday, E. and Noirhomme-Fraiture, M. (2008). Symbolic Data Analysis and the SODAS Software. John Wiley and Sons, England.
Francis, S. V. and Sasikala, M. (2013). Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features. Journal of Medical Engineering & Technology, 37(1):17–21.
Hayward, J. (1987). Uma vis˜ao global do problema. In MONTORO, A. F., GIANOTTI FILHO, O., FERREIRA, C. A., VALENTE, C. A., PETRILLI, C., POSSIK, R. A., and MéLEGA, J. M., editors, Alternativas Diagnósticas e Terapêuticas no Câncer de Mama, chapter 1, pages 3–11. BRADEPCA/CNPQ, S˜ao Paulo.
INCA (2007). Estimativas 2008: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2009). Estimativas 2010: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2011). Estimativas 2012: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2014). Estimativas 2014: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
Kapoor, P. and Prasad, S. V. A. V. (2010). Image processing for early diagnosis of breast cancer using infrared images. Proceedings of the 2010 IEEE Computer and Automation Engineering 2nd International Conference, 3:564–566.
Kuruganti, P. T. and Qi, H. (2002). Asymmetry analysis in breast cancer detection using thermal infrared images. In Proceedings of the Second Joint EMBS/BMES Conference, Houston, TX, USA.
Lahiri, B., Bagavathiappan, S., Jayakumar, T., and Philip, J. (2012). Medical applications of infrared thermography. Infrared Physics & Technology, 55:221–235.
Lu, J., Plataniotis, K. N., Venetsanopoulos, A. N., and Wang, J. (2005). An efficient kernel discriminant analysis method. Pattern Recognition, 38:1788–1790.
Mookiah, M. R. K., Acharya, U. R., and Ng, E. (2012). Data mining technique for breast cancer detection in thermograms using hybrid feature extraction strategy. Quantitative InfraRed Thermography Journal, 9(2):151–165.
Ng, E. Y. K. (2009). A review of thermography as promising non-invasive detection modality for breast tumor. International Journal of Thermal Sciences, 48:849–859.
Ng, E. Y. K., Kee, E., and Acharya, R. U. (2005). Advanced technique in breast thermography analysis. Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference.
Ng, E. Y. K. and Sudarshan, N. M. (2004). Computer simulation in conjunction with medical thermography as an adjunct tool for early detection of breast cancer. BMC Cancer, 4.
Oliveira, M. M. (2012). Development of a protocol and construction of a mechanical apparatus for standardizing the acquisition of breast thermographic images. Master’s thesis, Federal University of Pernambuco, Brazil. In Portuguese.
Schaefer, G., Závisek, M., and Nakashima, T. (2009). Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognition, 47:1133–1137.
Tan, T., Quek, C., Ng, G., and Ng, E. (2007). A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Systems with Applications.
Tang, X., Ding, H., Yuan, Y.-e., and Wang, Q. (2008). Morphological measurements of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. Biomedical Signal Processing and Control, 3:312–318.
Bezerra, L., Oliveira, M. M., Rolim, T. L., Conci, A., Santos, F. G. S., Lyra, P. R. M., and Lima, R. C. F. (2013a). Estimation of breast tumor thermal properties using infrared images. Signal Processing, 93:2851–2863.
Bezerra, L. A., Oliveira, M. M., Araújo, M. C., Viana, M. J. A., Santos, L. C., Santos, F. G. S., Rolim, T. L., Lyra, P. R. M., Lima, R. C. F., Borschartt, T. B., Resmini, R., and Conci, A. (2013b). Infrared imaging for breast cancer detection with proper selection of properties: From acquisition protocol to numerical simulation. In Ng, E. Y. K., Acharya, U. R., Rangayyan, R. M., and Suri, J. S., editors, Multimodality Breast Imaging: Diagnosis and Treatment, volume 1. SPIE PRESS.
da Cunha Queiroz, K. F. F. (2014). Análise da repetitividade e melhoria de segmentação semiautomática de rois em imagens termográficas de mama. Trabalho de conclus˜ao de curso (Monografia) – Universidade Federal de Pernambuco, Recife, 2014.
de Souza, R. M. C. R. (1999). Classificação de imagens sar baseada em uma abordagem simbólica. Master’s thesis, Universidade Federal de Pernambuco, Recife.
Diday, E. and Noirhomme-Fraiture, M. (2008). Symbolic Data Analysis and the SODAS Software. John Wiley and Sons, England.
Francis, S. V. and Sasikala, M. (2013). Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features. Journal of Medical Engineering & Technology, 37(1):17–21.
Hayward, J. (1987). Uma vis˜ao global do problema. In MONTORO, A. F., GIANOTTI FILHO, O., FERREIRA, C. A., VALENTE, C. A., PETRILLI, C., POSSIK, R. A., and MéLEGA, J. M., editors, Alternativas Diagnósticas e Terapêuticas no Câncer de Mama, chapter 1, pages 3–11. BRADEPCA/CNPQ, S˜ao Paulo.
INCA (2007). Estimativas 2008: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2009). Estimativas 2010: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2011). Estimativas 2012: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
INCA (2014). Estimativas 2014: Incidência de Câncer no Brasil. Ministério da Saúde, Rio de Janeiro, Brasil.
Kapoor, P. and Prasad, S. V. A. V. (2010). Image processing for early diagnosis of breast cancer using infrared images. Proceedings of the 2010 IEEE Computer and Automation Engineering 2nd International Conference, 3:564–566.
Kuruganti, P. T. and Qi, H. (2002). Asymmetry analysis in breast cancer detection using thermal infrared images. In Proceedings of the Second Joint EMBS/BMES Conference, Houston, TX, USA.
Lahiri, B., Bagavathiappan, S., Jayakumar, T., and Philip, J. (2012). Medical applications of infrared thermography. Infrared Physics & Technology, 55:221–235.
Lu, J., Plataniotis, K. N., Venetsanopoulos, A. N., and Wang, J. (2005). An efficient kernel discriminant analysis method. Pattern Recognition, 38:1788–1790.
Mookiah, M. R. K., Acharya, U. R., and Ng, E. (2012). Data mining technique for breast cancer detection in thermograms using hybrid feature extraction strategy. Quantitative InfraRed Thermography Journal, 9(2):151–165.
Ng, E. Y. K. (2009). A review of thermography as promising non-invasive detection modality for breast tumor. International Journal of Thermal Sciences, 48:849–859.
Ng, E. Y. K., Kee, E., and Acharya, R. U. (2005). Advanced technique in breast thermography analysis. Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference.
Ng, E. Y. K. and Sudarshan, N. M. (2004). Computer simulation in conjunction with medical thermography as an adjunct tool for early detection of breast cancer. BMC Cancer, 4.
Oliveira, M. M. (2012). Development of a protocol and construction of a mechanical apparatus for standardizing the acquisition of breast thermographic images. Master’s thesis, Federal University of Pernambuco, Brazil. In Portuguese.
Schaefer, G., Závisek, M., and Nakashima, T. (2009). Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognition, 47:1133–1137.
Tan, T., Quek, C., Ng, G., and Ng, E. (2007). A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Systems with Applications.
Tang, X., Ding, H., Yuan, Y.-e., and Wang, Q. (2008). Morphological measurements of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. Biomedical Signal Processing and Control, 3:312–318.
Published
2015-07-20
How to Cite
ARAÚJO, Marcus; LIMA, Rita; SOUZA, Renata.
Uso de imagens termográficas para classificação de anormalidades de mama. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 15. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 91-100.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2015.10369.
