Abstract
Nowadays, cancer is a major cause of women death, especially breast cancer which is most seen on ladies older than 40 years. As we know, several techniques have been developed to fight breast cancer, like a mammography, which is the preferred screening examination for breast cancer. However, despite mammography test showing negative result, there are still patients with breast cancer diagnostic, found by other tests like ultrasound test. It can be explained by potential side effect of using mammography, which can push patients and doctors to look for other diagnostic technique. In this literature review, we will explore the digital infrared imaging which is based on the principle that metabolic activity and vascular circulation, in both pre-cancerous tissue and the area surrounding a developing breast cancer, is almost always higher than in normal breast tissue. In the same way, an automated infrared image processing of patient cannot be done without a model like the hemispheric model, which is very well known. As novelty, we will give a comparative study of breast cancer detection using modern visual IT techniques view by the perspective of computer scientist.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breast Cancer Facts, National Breast Cancer Foundation (2016)
Dongola, N.: Mammography in Breast Cancer. Medscape Logo (2016)
Köşüş, N., Köşüş, A., Duran, M., Simavlı, S., Turhan, N.: Comparison of standard mammography with digital mammography and digital infrared thermal imaging for breast cancer screening. J. Turk. Ger. Gynecol. Assoc. (2010)
Li, S., Johnson, J., Peck, A., Xie, Q.: Near infrared fluorescent imaging of brain tumor with IR780 dye incorporated phospholipid nanoparticles. J. Trans. Med. (2017)
Amria, A., Pulko, S.H., Wilk, A.J.: Potentialities of steady-state and transient thermography in breast tumour depth detection: a numerical study. Comput. Methods Programs Biomed. (2016)
Boogerd, L.S.F., Handgraaf, H.J.M., Lam, H.-D., Huurman, V.A.L., Farina-Sarasqueta, A., Frangioni, J.V., van de Velde, C.J.H., Braat, A.E., Vahrmeijer, A.L.: Laparoscopic detection and resection of occult liver tumors of multiple cancer types using real-time near-infrared fluorescence guidance. Surg. Endosc. (2017)
Kandlikar, S.G., Perez-Raya, I., Raghupathi, P.A., Gonzalez-Hernandez, J.L., Dabydeen, D., Medeiros, L., Phatak, P.: Infrared imaging technology for breast cancer detection—Current status, protocols and new directions. Int. J. Heat Mass Trans. (2017)
Tsutomu Namikawa, T.S.: Recent advances in near-infrared fluorescence-guided imaging surgery using indocyanine green. Surg. Today (2015)
Kontos, M., Wilson, R., Fentiman, I.: Digital infrared thermal imaging (DITI) of breast lesions: sensitivity and specificity of detection of primary breast cancers. Clin. Radiol. (2011)
Łyszczarz, B., Nojszewska, E.: Productivity losses and public finance burden attributable to breast cancer in Poland, 2010–2014. BMC Cancer 17(1), 676 (2017)
National Oncology Program. Czech Oncological Society (2011)
Unar-Munguía, M., Meza, R., Colchero, M.A., et al.: Economic and disease burden of breast cancer associated with suboptimal breastfeeding practices in Mexico. Cancer Causes Control (2017)
Boquete, L., Ortega, S., Miguel-Jiménez, J.M., Rodríguez-Ascariz, J.M.: Automated detection of breast cancer in thermal infrared images, based on independent component analysis. J. Med. Syst. (2012)
Kubicek, J., Bryjova, I., Faltynova, K., Penhaker, M., Augustynek, M., Maresova, P.: Evaluation of gama analysis results significance within verification of radiation IMRT plans in radiotherapy. Lecture Notes in Computer Science, vol. 10449, pp. 541–548 (2017). https://doi.org/10.1007/978-3-319-67077-5_52
Augustynek, M., Korpas, D., Penhaker, M., Cvek, J., Binarova, A.: Monitoring of CRT-D devices during radiation therapy in vitro. BioMedical Engineering Online, 15 (1), article no. 29 (2016). https://doi.org/10.1186/s12938-016-0144-7
Smidova, I.: Alcohol and breast cancer—economic costs. Hygiena 51(1), 17–21 (2012)
Gustavsen, G., Schroeder, B., Kennedy, P., et al.: Health economic analysis of breast cancer index in patients with ER+, LN− breast cancer. Am. J. Manag. Care 20(8), 1 (2014)
Kim, Y.A., Oh, I.H., Yoon, S.J., et al.: The economic burden of breast cancer in Korea from 2007–2010. Cancer Res. Treat. 47(4), 583–590 (2015)
Bryjova, I., Kubicek, J., Molnarova, K., Peter, L., Penhaker, M., Kuca, K.: Multiregional segmentation modeling in medical ultrasonography: extraction, modeling and quantification of skin layers and hypertrophic scars. Lecture Notes in Computer Science, vol. 10449, LNAI, pp. 182–192 (2017). https://doi.org/10.1007/978-3-319-67077-5_18
IMS Health, MIDAS, Dec 2015; Market Prognosis, Mar 2016. IMS Institute for Healthcare Informatics, May 2016
Cardoso, F., Harbeck, N., Bergh, J., Cortés, J.: Research needs in breast cancer. Ann. Oncol. (2016)
Acknowledgements
This work was supported by internal students project at FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Mambou, S., Maresova, P., Krejcar, O., Selamat, A., Kuca, K. (2018). Breast Cancer Detection Using Modern Visual IT Techniques. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-76081-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76080-3
Online ISBN: 978-3-319-76081-0
eBook Packages: EngineeringEngineering (R0)