Diagnosis of Breast Cancer by Integrating Machine Learning and Machine Vision Techniques in Thermography Images

Document Type : Original Article


1 Faculty of Science and Technology of Organizational Resources, Amin University, Tehran, Iran

2 Faculty of Electrical and Computer Engineering, Al-Taha University, Tehran, Iran


Breast cancer has increased among women in recent years and is one of the leading causes of death in women. Studies show that thermography is a faster, cheaper, passive, risk-free, radiation-free and pain-free method than other diagnostic methods. New methods of image processing, vision and machine learning have led to successful investigations into the invention of breast cancer detection systems by thermometric images. In the present study, a proper method of diagnosing abnormality through thermography images of the obverse view is presented. By this segregation method, the breast area and every other area targeted by the physician that is vital for breast cancer diagnosis are color-divided in the thermographs. Warmer regions known as vital centers are extracted by the FCM algorithm and the fractal dimension of these regions is calculated using three different methods. The Studies suggesting that fractal analysis may potentially improve the reliability of thermography in breast tumor detection. The innovative aspect of this paper is the study of the role of fractal analysis in tracking the symmetrical heat distribution in two breast tissues in thermographic images. The results show that fractal analysis plays an important role in tracking the symmetrical heat distribution in two breast tissues to investigate asymmetry in order to detect breast abnormalities.


Main Subjects

1[ امیر ارجمند، سعید مشگینی، رضا افروزیان «آشکار سازی توده سرطانی پستان به کمک شبکه عصبی کانولوشنی در تصاویر ام.آر. آی» پردازش سیگنال پیشرفته، جلد 3، شماره 2، پاییز و زمستان 1398.
]2[ آذر محمدزاده، حامد آگاهی «بازشناسی ارقام دست­نویس فارسی مبتنی بر ترکیب ماشین­های بردار پشتیبان به روش فازی نوع دو بازه­ای»  پردازش سیگنال پیشرفته، جلد ۴، شماره ۲، پائیز و زمستان ۱۳۹۹، صفحات ۲62-251.
[3] P. Grassberger and I. Procaccia, “Measuring the Strangeness of Strange Attractors,” Physica D:Nonlinea Phenomena, vol. 9, no. 1, pp. 189-208, 1983.
[4] S. B. Fox, K. C. Gatter, R. D. Leek, A. L. Harris,  J. Bliss, J. L. Mansi, and B. Gusterson, “Association of tumor angiogenese with bone marow micromeetastase in breast cancer patiients,”  journal of the National center Institute, 1997.
[5] R. C. Gonzealez, and R. E. Woods, Digital Image Processing, 2nd ed., Prentice-Hall, Inc., 2002.
[6] G. Schaefer, S. Y. Zhu, and B. Jones, “An image retrieval approach for thermal medical images,” Proceedings of 8th Medical Image Understanding and Analysis, pp. 181-183, 2004
[7] Woods, R. C. G. a. R. E., Digital Image Processing, Prentice Hall, 2007.
[8] Kaihua Zhang a, L. Z. a., Huihui Song b, Wengang Zhou. Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing., 2008.
[9] Mahnaz EtehadTavakol, C. L., Saeed Sadri, E.Y. K. Ng. Analysis of Breast Thermography Using Fractal Dimension to Establish Possible Difference between Malignant and Benign Patterns. Healthcare Engineering, 27-43, 2010.
[10] HosseinGhayoumizadeh. Distinguish breast cancer based on thermal features in infrared images. researchgate. 2011.
[11] N. Selvarasu, A. N., and N. Nandhitha. Effective Representation of Non-Uniformity and Asymmetry in Breast Thermographs using Statistical Parameters on Histograms of Wavelet Coefficients for Cancer Detection. European Journal of Scientific Research, 80, 10-19, 2012.
[12] B. B. Lahiri, S. Bagavathiappan, T. Jayakumar, J. Philip, “Medical applications of infrared thermography: A review,” Infrared Physics & Technology, vol. 55, no. 4, pp. 221-235, 2012.
[13] T. B. Borchartt, A. Conci, R. C. F. Lima, R. Resmini, and A. Sanchez, “Breast thermography from an image processing viewpoint: A survey,” Signal Processing, vol. 93, no. 10, pp. 2785-2803, 2013.
[14] T. Banerjee, “Day or night Activity Recognition From Video Using Fuzzy Clustering Techniquew,” IEEE Transaction on Fuzzy systems, vol. 22, no. 3, pp. 483-493, 2014.
[15] M. Fatemeh Khosravi-Farsani, D. H. E.-K. Fully automatic breast segmentation of thermal images in order to aid diagnosis automatic breast cancer detection., 2014.
[16] Calder´on-Contreras, J. D., Chac´on-Murgu´ıa, M.I., Villalobos-Montiel, A.J.,Ortega-M´aynez.. A fuzzy computer aided diagnosis system using breast thermography. IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015.
[17] Gogoi, U.R., Majumdar, G., Bhowmik, M.K., Ghosh, A.K., Bhattacharjee, D. Breast abnormality detection through statistical feature analysis using infrared thermograms, in: International Symposium on Advanced Computing and Communication (ISACC), IEEE. pp. 258–265, 2015.
[18] Lessa, V., Marengoni, M., Applying artificial neural    network for the classification of breast cancer using infrared thermographic images, in: International
Conference on Computer Vision and Graphics, Springer. pp. 429–438., 2016.
[19] Sayed, G. I., Soliman, M., & Hassanien, A. E. Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection. springer International Publishing, 2016.
[20] Gogoi, U.R., Bhowmik, M.K., Ghosh, A.K., Bhattacharjee, D., Majumdar,G., 2017. Discriminative feature selection for breast abnormality detection and accurate classification of thermograms, in: 2017 International Conference on Innovations in Electronics, Signal Processing and Communication.
[21] Gehad Ismail Sayed, Alaa Tharwat, Aboul Ella Hassanien,2018. Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Springer Science+Business Media.
[22] Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., 2019. Role of normalization of breast thermogram images and automatic classification of breast cancer. The Visual Computer 35, 57–70.
[23] Singh, D., & Singh, A. K.. Role of image thermography in early breast cancer detection- Past, present and future. Computer Methods and Programs in Biomedicine, 183, 105074, 2020.
[24] Sánchez-Ruiz , D., Olmos-Pineda, Ivan,Olvera-López, J. Arturo. Automatic region of interest segmentation for breast thermogram image classification. Pattern Recognition Letters, 135, 72-81, 2020..
[25] Roberto, G. F., Lumini, A., Neves, L. A., & do Nascimento, M. Z. (2021). Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images. Expert Systems with Applications, 166, 114103.