Abstract
The use of thermal imaging for disease diagnosis has been successful. This study explores the impact of considering bilateral symmetry in thermography. Concretely: it consider if there is statistically difference in the results of a diagnosis with inclusion of texture symmetry. For this, symmetry analysis was conducted on three levels: thorax sides, breast regions, and quadrants using thermograms from the DMR-IR database. Haralick (H) descriptors and local binary patterns (LBP) were computed to be used as texture features. Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) techniques were employed to classify them as from cancer or normal images. Accuracy was used for evaluation, followed by post-hoc analysis using Friedman and Nemenyi tests. The results were conclusive, with a \(p<0.05\), allowing the rejection of the null hypotheses \(H_{0}\) at all levels and confirming that including symmetry consideration in the feature vector led to statistically significant differences in the results. With \(p<0.01\), this was observed for the majority of the cases at the thorax side and breast levels. Taking into account the small number of samples (100) used, we can concluded that symmetry has an impact on the cancer diagnostic result using infrared images, but this impact on numbers should be better analyzed in future works. All developments, from the breast segmentation and quadrants masks up to the computer code, are publicly available in a GitHub repository.
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References
Borchartt, T.B., et al.: Combining approaches for early diagnosis of breast diseases using thermal imaging. Int. J. Innovative Comput. Appl. 4(3–4), 163–183 (2012). https://doi.org/10.1504/IJICA.2012.050054
Borchartt, T.B., Conci, A., Lima, R.C.F., Resmini, R., Sanchez, A.: Breast thermography from an image processing viewpoint: a survey. Signal Process. 93(10), 2785–2803 (2013). https://doi.org/10.1016/j.sigpro.2012.08.012
Rodrigues, E. O., Conci, A., Borchartt, T. B., Paiva, A. C., Silva, A. C., MacHenry, T.: Comparing Results of Thermographic Images Based Diagnosis for Breast Diseases. arXiv preprint arXiv:2208.14410 (2022)
Damiao, C.P., et al.: On the possibility of using temperature to aid in thyroid nodule investigation. Sci. Rep. 10(1), 1–10 (2020). https://doi.org/10.1038/s41598-020-78047-1
Gonçalves, C.B., Souza, J.R., Fernandes, H.: CNN optimization using surrogate evolutionary algorithm for breast cancer detection using infrared images. In: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), Shenzen, China, pp. 84–89 (2022). https://doi.org/10.1109/CBMS55023.2022.00022
Figueiredo, A.A.A., do Nascimento, J.G., Malheiros, F.C., da Silva Ignacio, L.H., Fernandes, H.C., Guimaraes, G.: Breast tumor localization using skin surface temperatures from a 2D anatomic model without knowledge of the thermophysical properties. Comput. Methods ProgramsBiomed. 172, 65–77 (2019)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2016)
Silva, L.F., et al.: A new database for breast research with infrared image. J. Med. Imaging Health Inform. 4(1), 92–100 (2014). https://doi.org/10.1166/jmihi.2014.1226
Silva, L.F., Santos, A.A.S.M.D., Bravo, R.S., Silva, A.C., Muchaluat-Saade, D.C., Conci, A.: Hybrid analysis for indicating patients with breast cancer using temperature time series. Comput. Methods Programs Biomed. 130, 142–153 (2016). https://doi.org/10.1016/j.cmpb.2016.03.002
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36
Bastos, L. de O., Liatsis, P., Conci, A.: Automatic texture segmentation based on k-means clustering and efficient calculation of co-occurrence features. In: Proceedings do 2008 15th International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, pp. 141–144 (2008). https://doi.org/10.1109/IWSSIP.2008.4604387
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314
dos Santos Araujo, A., Conci, A., Hernandez Moran, M.B., Muchaluat-Saade, D.C., Resmini, R.: On computation of texture descriptors from sum and difference histograms. In: Proceedings do 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), Maribor, Slovenia, pp. 1–5. (2018). https://doi.org/10.1109/IWSSIP.2018.8439522
Coelho, L.P.: Mahotas: open source software for scriptable computer vision. J. Open Res. Softw. 1(1), e3 (2013). https://doi.org/10.5334/jors.ac
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623
van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T.: scikit-image contributors: scikit-image: Image processing in Python. PeerJ 2, e453 (2014). https://doi.org/10.7717/peerj.453
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. Adaptive Computation and Machine Learning series. MIT Press (2012)
Virtanen, P., Gommers, R., Oliphant, T.E. et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2
Fernandez, A.: Mastering OpenCV 4 with Python. Packt Publishing Ltd. (2019)
Ben-Hur, A., Weston, J.: A user’s guide to support vector machines. Methods Mol. Biol. 609, 223–39. PMID: 20221922 (2010). https://doi.org/10.1007/978-1-60327-241-4_13
scikit-learn. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830 (2011)
Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley Series in Probability and Statistics. Wiley (2013)
Harter, H.L.: Tables of range and studentized range. Ann. Math. Stat. 31(4), 1122–1147 (1960). https://doi.org/10.1214/aoms/1177705684
Princigalli, N.: Symmetry Analysis in Thermography: An application in breasts (in Portuguese: Análise de Simetria em Termografias: uma aplicação em mamas). (Final course monograph), GitHub (2023). https://github.com/nuba/uff-2023-tcc-simetria-mamas
Silva, L., Seixas, F., Fontes, C., Muchaluat-Saade, D., Conci, A.A.: Computational method for breast abnormality detection using thermographs. In Proceedings do 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA. pp. 469–474 (2020). https://doi.org/10.1109/CBMS49503.2020.00095
Digit3D projects. https://sites.google.com/view/three-dimensional-breast-model
Conci, A., Kubrusly, C.: Distances between sets - a survey. Advances in Mathematical Sciences and Applications, vol. 26(1), pp. 1–18 (2017)
Acknowledgments
E.L.S.M. is supported by Federal Institute of Education, Science and Technology of Rondônia (IFRO). A.C. is supported in part by CYTED, the National Institutes of Science and Technology (INCT - MACC project), National Council for Scientific and Technological (CNPq) under grant 307638/2022-79, the Research Support Foundation of Rio de Janeiro State (FAPERJ) over CNE, SIADE-2, e-Health Rio and Digit3D (“tematico”) projects [26].
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Princigalli, N., Moura, E.L.S., Conci, A. (2023). Could the Consideration of Symmetry be Statistically Significant for Breast Infrared Analysis?. In: Kakileti, S.T., Manjunath, G., Schwartz, R.G., Frangi, A.F. (eds) Artificial Intelligence over Infrared Images for Medical Applications. AIIIMA 2023. Lecture Notes in Computer Science, vol 14298. Springer, Cham. https://doi.org/10.1007/978-3-031-44511-8_5
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