Abstract
Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.
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References
Breast Cancer India : Pink Indian Statistics. Available at: http://www.breastcancerindia.net/statistics/stat_global.html [Accessed 14 Apr. 2019].
Kandlikar S., Perez-Raya I., Raghupathi P.G., 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. 108: 2303–2320, 2017. https://doi.org/10.1016/j.ijheatmasstransfer.2017.01.086
Sathish D., Kamath D., Rajagopal K.V., Prasad K., Medical imaging techniques and computer aided diagnostic approaches for the detection of breast cancer with an emphasis on thermography - a review. Int. J. Med. Eng. Inform. 8: 275–99, 2016. https://doi.org/10.1504/IJMEI.2016.077446
Ng E.Y.K., Sudharsan N.M., Numerical computation as a tool to aid thermographic interpretation. J. Med. Eng. Technol. 25 (2): 53–60, 2001. https://doi.org/10.1080/03091900110043621
Kennedy D.A., Lee T., Seely D. (2009) A comparative review of thermography as a breast cancer screening technique. Integra. Cancer Therap. 9–16 https://doi.org/10.1177/1534735408326171
Sree S.V., Ng E.Y.-K., Rajendra A.U., Tan W., Breast imaging systems: a review and comparative study. J. Mechan. Med. Bio. 10: 5–34, 2010. https://doi.org/10.1142/S0219519410003277
DMR-IR. Available at: http://visual.ic.uff.br/dmi [online] [Accessed 16 Apr. 2019].
Irvine J.M., Targeting breast cancer detection with military Mag. IEEE, Eng. Med. Biol. Mag. 21 (6): 36–40, 2002. https://doi.org/10.1109/MEMB.2002.1175136
U.S. Food and Drug Administration. Breast Cancer Screening—Thermography Is Not an Alternative to Mammography: FDA Safety Communication. Available at: https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm257633.htm. Date posted: 6/2/2011. [Accessed March 3, 2019.]
Jones B.F., A reappraisal of the use of infrared thermal image analysis in medicine. IEEE Trans. Med. Imaging 17 (6): 1019–1027, 1998. https://doi.org/10.1109/42.746635
Gamagami P. (1996) Indirect Signs of Breast Cancer : Angiogenesis study, Atlas of Mammography, Blackwell Science, Cambridge
Keyserlingk J., Ahlgren P., Yu E., Belliveau N., Infrared imaging of the breast: initial reappraisal using High-Resolution digital technology in 100 successive cases of stage I and II breast cancer. Breast J. 4: 245–251, 1998. https://doi.org/10.1046/j.1524-4741.1998.440245.x
Neal C.H., flynt K.A., Jeffries D.O., Helvie M.A., Breast Imaging Outcomes following Abnormal Thermography. Acad. Radiol. 25 (3): 273–278, 2018. https://doi.org/10.1016/j.acra.2017.10.015
M/s Tuscano Systems Pvt Ltd: Mammary rotational infrared thermographic system [MAMRIT] PCT/IN 2012/000778 (2012)
Joseph D., Bronzino, 3rd edition. Boca Raton: CRC Press, 2006
Anbar M., Milescu L., Naumov A., Brown C., Button T., Carly C., AlDulaimi K., Detection of cancerous breasts by dynamic area telethermometry. IEEE Eng. Med. Biol. Mag. 20: 80–91, 2001. https://doi.org/10.1109/51.956823
Keith L., Oleszczuk J., Laguens M., Circadian rhythm chaos: a new breast cancer marker. Int. J. Fert. Women’s Med. 46: 238–247, 2001
Lipari C.A., Head J.F. (1997) Advanced infrared image processing for breast cancer risk assessment. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2. 673–676. 10.1109/IEMBS.1997.757713.
Francis S.V., Sasikala M., Bharathi G.B., Jaipurkar S.D., Breast cancer detection in rotational thermography images using texture features. Infra. Phys. Technol. 67: 490–496, 2014. https://doi.org/10.1016/j.infrared.2014.08.019
Marques R.de.S., [automatic segmentation of thermal mammogram images, dissertation].. In: Instituto de Computação Universidade Federal Fluminense. Instituto de Computação Universidade Federal Fluminense, Portuguese, 2012
Silva L.F., Saade D.C.M., Sequeiros G.O., Silva A.C., Paiva A.C., Bravo R.S., Conci A., 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
Venkataramani K., Jabbireddy S., Madhu H.J., Kakileti S.T. (2017) US Patent Application No. 9,865,052
NoTouch BreastScan [Online] Available at: http://www.notouchbreastscan.com/index.html [Accessed 8 Apr. 2019]
Wishart G.C., Campisi M., Boswell M., Chapman D., Shackleton V., Iddles S., Hallett A., Britton P.D., The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. Europ. J. Surg. Oncol. (EJSO) 36 (6): 535–540, 2010. https://doi.org/10.1016/j.ejso.2010.04.003
Koprowski R., Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing. Biomed. Eng. 13(1):1–21, 2014. https://doi.org/10.1186
Das K., Majumdar G., Bhowmik M.K. (2017) Qualitative measures of breast thermograms towards abnormality prediction. 8th International Conference on Computing. Commun. Netw. Technol. (ICCCNT) 1–6
Kafieh R., Rabbani H. (2011) Wavelet-based medical infrared image noise reduction using local model for signal and noise. IEEE Statis. Signal Process. Works. 549–552 https://doi.org/10.1109
Lin C.L., Chang Y.C., Kuo C.W., Huang H.M., Jian E.L. (2010) A fast denoising approach to corrupted infrared images. Int. Conf. Syst. Sci. Eng. (ICSSE) 207–211 https://doi.org/10.1109
Serrano R., Ulysses C., Ribeiro J., Lima R.C.F. (2010) Using Hurst coefficient and Lacunarity for diagnosis of breast diseases considering thermal images. Proc. of 17th International Conference on Systems Signals Image Process. 550–553
Sathees P.C., Sujatha M., Swaminathan R. (2014) Asymmetry analysis of breast thermograms using BM3d technique and statistical texture features. 2014 International Conference on Informatics. Electro. Vision (ICIEV) 1–4 https://doi.org/10.1109/ICIEV.2014.6850730
Shahari S., Wakankar A. (2015) Color analysis of thermograms for breast cancer detection. Int. Conf. Indust. Instrumen. Control (ICIC) 1577–1581 https://doi.org/10.1109/IIC.2015.7151001
Kapoor P., Prasad S.V.A.V., Image processing for early diagnosis of breast cancer using infrared images. 2nd Int. Conf. Comput. Autom. Eng. (ICCAE) 3: 564–566, 2010. https://doi.org/10.1109/ICCAE.2010.5451827
EtehadTavakol M., Chandran V., Ng E.Y.K., Kafieh R., Breast cancer detection from thermal images using bispectral invariant features. Int. J. Thermal Sci. 69: 21–36, 2013. https://doi.org/10.1016/j.ijthermalsci.2013.03.001
Silva L.F., Saade D.C.M., Sequeiros G.O., Silva A.C., Paiva A.C., Bravo R.S., Conci A., 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
Dayakshini D., Kamath S., Prasad K., Rajagopal K.V., Segmentation of breast thermogram images for the detection of breast cancer – a projection profile approach. J. Image Graph. 3 (1): 47–51, 2015
Zare I., Evaluating the thermal imaging system in detecting certain types of breast tissue masses. Biomed. Res. India 27: 670–675, 2016
Madhavi V., Bobby C., Assessment of Dynamic Infrared Images for Breast Cancer Screening using BEMD and URLBP. Int. J. Pure Appl. Math. 114 (10): 261–269, 2017
Hankare P., Shah K., Nair D., Nair D., Breast cancer detection using thermography. Int. Res. J. Eng. Technol. (IRJET). 3 (4): 1061–1064, 2016
Angeline Kirubha S.P., Anburajan M., Venkataraman B., Menaka M., A case study on asymmetrical texture features comparison of breast thermogram and mammogram in normal and breast cancer subject. Biocatal. Agricult. Biotechnol. 15: 390–401, 2018. https://doi.org/10.1016/j.bcab.2018.07.001
Golestani N., Tavakol E.M., Ng E.Y.K., Level set method for segmentation of infrared breast thermograms. Experiment. Clinic. Sci. 13: 241–251, 2014. https://doi.org/10.17877/DE290R-15979
de Oliveira J.P.S., Conci A., Prez M.G., Andaluz V.H. (2015) Segmentation of infrared images: a new technology for early detection of breast diseases. IEEE Int. Conf. Indust. Technol. (ICIT) 1765–1771
Min S., Heo J., Kong Y., Nam Y., Ley P., Jung B.-K., Dongik O.H., Shin W., Thermal infrared image analysis for breast cancer detection. KSII Trans. Internet Inform. Syst. 11 (2): 1134–1147, 2017. https://doi.org/10.3837/tiis.2017.02.029
Pramanik S., Bhattacharjee D., Nasipuri M. (2015) Wavelet based thermogram analysis for breast cancer detection. Int. Symp, Adv. Comput. Commun. (ISACC) 205–212 https://doi.org/10.1109/ISACC.2015.7377343
Ali M.A.S., Sayed G.I., Gaber T., Hassanien A.E., Snasel V., Silva L.F. (2015) Detection of breast abnormalities of thermograms based on a new segmentation method. Feder. Conf.Comput. Sci. Inform. Syst. (FedCSIS) 255–261 https://doi.org/10.15439/2015F318
Prabha S., Anandh K., Sujatha C., Ramakrishnan S. (2014) Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms. Eng. Med. Biol. Soc. (EMBC), 36th Annual Inte. Conf. IEEE. 6438–6441 https://doi.org/10.1109/EMBC.2014.6945102
Suganthi S., Ramakrishnan S., Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets. Biomed. Signal Process. Control 10:128–136, 2014. https://doi.org/10.1016/j.bspc.2014.01.008
Ng E.Y.K., Chen Y., Segmentation of breast thermogram: improved boundary detection with modified snake algorithm. J. Mech. Med. Biol. 6(2):123–136, 2006. https://doi.org/10.1142/S021951940600190X
Jeyanathan J., Jeyashree P., Shenbagavalli A., Transform based Classification of Breast Thermograms using Multilayer Perceptron Back Propagation Neural Network. Int. J. Pure Appl. Math. 118: 1955–1961, 2018
Garduño-Ramón M.A., Vega-Mancilla S.G., Morales-Henández L.A., Osornio-Rios R.A. (2017) Supportive Noninvasive Tool for the Diagnosis of Breast Cancer Using a Thermographic Camera as Sensor. Sensors (Basel, Switzerland) 17(3) https://doi.org/10.3390/s17030497
Head J.F., Lipari C.A., Elliot R.L., Computerized image analysis of digitized infrared images of breasts from a scanning infrared imaging system Proc SPIE. Infr. Technol. Appl. XXIV (3436): 290–294, 1998. https://doi.org/10.1117/12.328078
Head J.F., Wang F., Lipari C.A., Elliott R.L., The important role of infrared imaging in breast cancer. IEEE Eng. Med. Biol. Mag. 19(3):52–57, 2000. https://doi.org/10.1109/51.844380
Jakubowska T., Wiecek B., Wysocki M., Drews-Peszynski C., Thermal signatures for breast cancer screening comparative study. Proc. 25th Annual Int. Conf. IEEE Eng. Med. Biol. Soc. 2:1117–1120, 2003
Wang J., Chang K.J., Chen C.Y., Chien K.L., Tsai Y.S., Wu Y.M., Teng Y.C., Shih T.T., Evaluation of the diagnostic performance of infrared imaging of the breast: a preliminary study. Biomed. Eng. Online 9:3, 2010. https://doi.org/10.1186/1475-925X-9-3
Qi H., Snyder W., Head J., Elliott R., Detecting breast cancer from infrared images by asymmetry analysis. Proc. 22nd Annual Int. Conf. IEEE, Eng. Med. Biol. Soc. 2:1227–1228, 2000. https://doi.org/10.1109/IEMBS.2000.897952
Kuruganti P.T., Qi H., Asymmetry analysis in breast cancer detection using thermal infrared images. Proc. Second Joint 24th Annual Conf. Annual Fall Meet. Biomed. Eng. Soc. 2:1155–1156, 2002. https://doi.org/10.1109/IEMBS.2002.1106323
Mejia T., Perez M., Andaluz V., Conci A. (2015) Automatic segmentation and analysis of thermograms using texture descriptors for breast cancer detection Computer aided system engineering (APCASE) Asia-Pacific conference https://doi.org/10.1109/APCASE.2015.12
Kapoor P., Prasad D.S., Patni S., Automatic Analysis of Breast Thermograms for tumor detection based on Bio-statistical feature extraction and ANN. Int. J. Emerg. Trends Eng. Develop. 2(7):245–255, 2012
Gogoi U.R., Majumdar G., Bhowmik M.K., Ghosh A.K., Bhattacharjee D. (2015) Breast abnormality detection through statistical feature analysis using infrared thermograms. Int. Sympos. Adv. Comput. Commun. (ISACC) 258–265 https://doi.org/10.1109/ISACC.2015.7377351
Rassiwala M., Mathur P., Mathur R., Farid K., Shukla S., Gupta P.K., Jain B., Evaluation of digital infra–red thermal imaging as an adjunctive screening method for breast carcinoma: a pilot study. Int. J. Surg. 12(12):1439–1443, 2014. https://doi.org/10.1016/j.ijsu.2014.10.010
Tang X., Ding H., Yuan Y., Wang Q., Morphological measurement of localized temperature increase amplitudes in breast infrared thermograms and its clinical application. Biomed. Signal Process. Cont. 3(4):312–318. , 2008 . https://doi.org/10.1016/j.bspc.2008.04.001
Ghayoumi Z., Hossein H., Javad Seryasat O.R., Mostafav I., Mohammad S., Segmenting breast cancerous regions in thermal images using fuzzy active contours. EXCLI J. 15:532–550, 2016. https://doi.org/10.17877/DE290R-17666
Ng E., Ung L., Ng F., Sim L.S.G., Statistical analysis of healthy and malignant breast thermography. J. Med. Eng. Technol. 25:253–63, 2001. https://doi.org/10.1080/03091900110086642
EtehadTavakol M., Ng E., lucas C., sadri S, gheissari N., Estimating the Mutual Information Between Bilateral Breast in Thermograms Using Nonparametric Windows. J. Med. Syst. 35(5):959–967, 2011. https://doi.org/10.1007/s10916-010-9516-x
Heriana O., Soesanti I. (2015) Tumor size classification of breast thermal image using fuzzy C-Means algorithm. International Conference on Radar, Antenna. Microwave, Electron. Telecommun. 98–103 https://doi.org/10.1109/ICRAMET.2015.7380782
EtehadTavakol M., Lucas C., Sadri S., Ng E., Analysis of Breast Thermography Using Fractal Dimension to Establish Possible Difference between Malignant and Benign Patterns. J. Healthcare Eng. 1:27–44, 2010. https://doi.org/10.1260/2040-2295.1.1.27
Singletary S., Allred C., Ashley P., Bassett L., Berry D., Bland K., Borgen P., Clark G., Edge S., Hayes D., Hughes L., Hutter R., Morrow M., Page D., Recht A., Theriault R., Thor A., Weaver D., Wieand H., Greene F., Staging system for breast cancer: Revisions for the 6th edition of the AJCC cancer staging manual. Surg. Clinics North Ame. 83:803–19, 2003. https://doi.org/10.1016/S0039-6109(03)00034-3
Scales N., Herry C., Frize M., Automated image segmentation for breast analysis using infrared images. Annual Int. Conf. IEEE Eng. Med. Biol. Soc. 3:1737–40, 2004. https://doi.org/10.1109/IEMBS.2004.1403521
Kapoor P., Prasad S., Patni S., Image segmentation and asymmetry analysis of breast thermograms for tumor detection. Int. J. Comput. Appl. 50:40–45, 2012. https://doi.org/10.5120/7803-0932
Sarigoz T., Ertan T., Topuz O., Sevim Y., Cihan Y., Role of digital infrared thermal imaging in the diagnosis of breast mass: A pilot study: Diagnosis of breast mass by thermography. Infrared Phys. Technol. 91:214–219, 2018. https://doi.org/10.1016/j.infrared.2018.04.019
Jonathan H.F., Wang R.E., Breast thermography is a noninvasive prognostic procedure that predicts tumor growth rate in breast cancer patients. Annals of the New York Academy of Sciences 698:153–158, 1993. https://doi.org/10.1111/j.1749-6632.1993.tb17203.x
Ng E., Fok S.C., Peh Y.C., Ng F.C., Sim L.S.J., Computerized detection of breast cancer with artificial intelligence and thermograms. J. Med. Eng. Technol. 26:152–157, 2009. https://doi.org/10.1080/03091900210146941
Qi H., Head J.F., Asymmetry analysis using automatic segmentation and classification for breast cancer detection in thermograms. Proc. 23rd Annual Int. Conf. IEEE Eng. Med. Biol. Soc. 3:2866–2869, 2001. https://doi.org/10.1109/IEMBS.2001.1017386
EtehadTavakol M., Sadri S., Ng E.Y.K., Application of K- and Fuzzy c-Means for Color Segmentation of Thermal Infrared Breast Images. J. Med. Syst. 34(1):35–42, 2010. 10.1007/s10916-008-9213-1
Meena. R., Bhuvaneshwari K., Divya M., Sri K., Begum A. (2017) Segmentation of thermal infrared breast images using K-means, FCM and EM algorithms for breast cancer detection. Int. Conf. Innovat. Inform., Embedded Commun. Syst. (ICIIECS) 1-4 https://doi.org/10.1109/ICIIECS.2017.8276142
Nicandro C.R., Efrén M.M., MaríaYaneli A.A., Enrique M.D.C.M., Héctor Gabriel A.M. (2013) Evaluation of the diagnostic power of thermography in breast cancer using bayesian network classifiers. Comput. Math. Methods Med. 1-10 https://doi.org/10.1155%2F2013%2F264246
Mahmoudzadeh E., Montazeri M.A., Zekri M., Sadri S., Extended hidden Markov model for optimized segmentation of breast thermography images. Infra. Phys. Technol. 72:19–28, 2015. https://doi.org/10.1016/j.infrared.2015.06.012
Mohamed N.A., Breast cancer risk detection using digital infrared thermal images. Int. J. Bioinform. Biomed. Eng. 1(2):185–194, 2015
Mambou S.J., Maresova P., Krejcar O., Selamat A., Kuca K., Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model. Sensor (Basel Switzerland) 18(9):2799, 2018. https://doi.org/10.3390/s18092799
Santana M., Pereira J., Monica D., Silva F., Lima N., Sousa F., Arruda G., Lima R., Azevedo W., Dos Santos W., Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res. Biomed. Eng. 34(1):45–53, 2018. https://doi.org/10.1590/2446-4740.05217
Lashkari A., Pak F., Firouzmand M., Full Intelligent Cancer Classification of Thermal Breast Images to Assist Physician in Clinical Diagnostic Applications. J. Med. Signals Sensors 6(1):12–24, 2016. https://doi.org/10.4103/2228-7477.175866
Schaefer G., Závišek M., Nakashima T., Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognition 42(6):1133–1137, 2009. https://doi.org/10.1016/j.patcog.2008.08.007
Francis S., Mohan S., Saranya S., Detection of Breast Abnormality from Thermograms Using Curvelet Transform Based Feature Extraction. J. Med. Syst. 38:23, 2014. https://doi.org/10.1007/s10916-014-0023-3
Zadeh G., Haddadnia H., Hashemian J., Kazem M.H., Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging. Iranian J. Med. Phys. 9(4):265–274, 2012. https://doi.org/10.22038/ijmp.2013.470
Hossein Z.G., Diagnosing breast cancer with the aid of fuzzy logic based on data mining of a genetic algorithm in infrared images. Middle East J. Cancer 3:119–129, 2011
Tan T.Z., Quek C., Ng G., Ng E., A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Systems with Applications 33(3):652–666, 2007. https://doi.org/10.1016/j.eswa.2006.06.012
Fok S.C., Ng E., Tai K., Early detection and visualization of breast tumor with thermogram and neural network. J. Mech. Med. Biol. 2(2):185–195, 2011. https://doi.org/10.1142/S0219519402000344
Tan J.M.Y., Ng E.Y.K., Acharya R., Keith L.G., Holmes J., Comparative study on the use of analytical software to identify the different stages of breast cancer using discrete temperature data. J Med Syst 33(2):141–153, 2008. https://doi.org/10.1007/s10916-008-9174-4
Szu H., Kopriva I., Hoekstra P., Diakides N., Diakides M., Buss J., Lupo J., Early Tumor Detection by Multiple Infrared Unsupervised Neural Nets Fusion. Annual Int. Conf. IEEE Eng. Med. Biol. 2:1133–1136, 2003. https://doi.org/10.1109/IEMBS.2003.1279448
Jakubowska T.B., Wiecek M., Wysocki C., Drews-Peszyński Strzelecki M. (2004) Classification of Breast Thermal Images using Artificial Neural Networks. J. Med. Inform. Technol. 41–49 https://doi.org/10.1109/IEMBS.2004.1403370
Borchartt T., Resmini R., Conci A., Martins A., Silva A., Diniz E., Paiva A., Lima R. (2011) Thermal feature analysis to aid on breast disease diagnosis Proceedings of 21st Brazilian congress of mechanical engineering
Koay J., Herry C., Frize M., Analysis of breast thermography with an artificial neural network. Annual Int. Conf. IEEE Eng. Med. Biol. Soc. 2:1159–1162, 2004. https://doi.org/10.1109/IEMBS.2004.1403371
Acharya U., Rajendra N.G., Eddie T., Jen Jong S., Vinitha S., Thermography based breast cancer detection using texture features and support vector machine. J. Med. Syst. 36:1503–1510, 2010. https://doi.org/10.1007/s10916-010-9611-z
Madhu H., Kakileti S.T., Venkataramani K., Jabbireddy S. (2016) Extraction of medically interpretable features for classification of malignancy in breast thermography. 2016 38th Annual Int. Conf. IEEE Eng. Med. Biol.Society (EMBC) 1062–1065 https://doi.org/10.1109/EMBC.2016.7590886
Gogoi U., Majumdar G., Bhowmik M., Ghosh A., Evaluating the efficiency of infrared breast thermography for early breast cancer risk prediction in asymptomatic population. Infrared Phys. Technol. 99:201–211, 2019. https://doi.org/10.1016/j.infrared.2019.01.004
Zuluaga J.P.A., Masry Z., Benaggoune K., Meraghni S.Z., Noureddine A. (2019) CNN-based methodology for breast cancer diagnosis using thermal images. arXiv:1910.13757
Dalmia A., Kakileti S.T., Manjunath G. (2018) Exploring deep learning networks for tumour segmentation in infrared images. 14th Quantitative InfraRed Thermography Conference https://doi.org/10.21611/qirt.2018.052
Krawczyk B., Schaefer G., Breast Thermogram Analysis Using Classifier Ensembles and Image Symmetry Features. IEEE Syst. J. 8(3):921–928, 2013. https://doi.org/10.1109/JSYST.2013.2283135
Pennes H.H., Analysis of tissue and arterial blood temperatures in the resting human forearm. J. Appl. Physiol. 85(1):5–34, 1948. https://doi.org/10.1152/jappl.1998.85.1.5
EtehadTavakol M., Ng E., Lucas C., Sadri S., Ataei M., Nonlinear analysis using Lyapunov exponents in breast thermograms to identify abnormal lesions. Infrar. Phys. Technol. 55:345–352, 2012. https://doi.org/10.1016/j.infrared.2012.02.007
Sudharsan N., Ng E., Teh S., Surface Temperature Distribution of a Breast With and Without Tumour. Comp. Meth. Biomechan. Biomed. Eng. 2(3):187–199, 1999. https://doi.org/10.1080/10255849908907987
Sudharsan N.M., Ng E.Y.K., Parametric optimization for tumour identification: bioheat equation using ANOVA and the Taguchi method. Proceedings of the Institution of Mechanical Engineers. Part H, J. Eng. Med. 214(5):505–512, 2000. https://doi.org/10.1007/s11517-005-0006-0
Ng E., Sudharsan N., An improved 3-D direct numerical modelling and thermal analysis of a female breast with tumour. Proceedings of the Institution of Mechanical Engineers. Part H, J. Eng. Med. 215(1):25–37, 2001. https://doi.org/10.1243/0954411011533508
Amri A., Wilkinson A., Pulko S., Potentialities of Dynamic Breast Thermography. Application of Infrared to Biomedical Sciences Berlin Heidelberg: Springer, 2017, pp 79–107 . https://doi.org/10.1007/978-981-10-3147-2_7.2017
Amri A., Pulko S., Wilkinson A., Potentialities of steady-state and transient thermography in breast tumour depth detection: a numerical study. Comput. Meth. Pro. Biomed. 123:68–80, 2015. https://doi.org/10.1016/j.cmpb.2015.09.014
Chanmugam A., Hatwar R., Herman C., Thermal Analysis of Cancerous Breast Model. ASME Int. Mechan. Eng. Cong. Expos. Proc. (IMECE) 2:134–143, 2012. https://doi.org/10.1115/IMECE2012-88244
Hatwar R., Herman C., Inverse method for quantitative characterization of breast tumors from surface temperature data. Int. J. Hyperther. 33(7):741–757, 2017. https://doi.org/10.1080/02656736.2017.1306758
Barnes R.B. (1963) United States Patent 3,245,402, Process Of diagnosis by infrared thermography., Stamford, Conn., assignor to Barnes Engineering Company, Stamford, Conn., a corporation of Delaware No Drawing. Filed May 21, Ser. No. 281, 984. https://patents.google.com/patent/US3245402A/en
Venkataramani K. (2016) Detecting tumorous breast tissue in a thermal image, Niramai Health Analytix Pvt. Ltd, US9486146B. https://patents.google.com/patent/US9486146/en
Venkataramani K., Jabbireddy S., Madhu H.J., Kakileti S.T. (2017) Contour-based determination of malignant tissue in a thermal image, United States, Niramai Health Analytix Pvt. Ltd (Bangalore, IN), 2017027065. http://www.freepatentsonline.com/y2017/0270659.html
Danicic A., (2016) Methods for thermal breast cancer detection, United States, WO2017184201A1. https://patents.google.com/patent/WO2017184201A1/en
Venkataramani; Krithika; (Bangalore, IN) ; Kakileti; Siva Teja; (Kakinada, IN) ; Madhu; Himanshu J.; (Mumbai, IN), 2017, Classifying hormone receptor status of malignant tumorous tissue from breast thermographic images, Niramai Health Analytix Pvt. Ltd, United States, 62356208, http//:shorturl.at/FQW15
Kakileti S.T., (2018) Blood vessel extraction in two-dimensional thermography, United States, 62356238, http//:shorturl.at/cRWX4
Venkataramani, Krithika (Bangalore, IN), Jabbireddy, Susmija (Hyderabad, IN) Madhu, Himanshu J.(Mumbai, IN) Kakileti, Siva Teja (Kakinada, IN), Ramprakash, Hadonahalli V. (Bangalore, IN) Thermography-based breast cancer screening using a measure of symmetry, Niramai Health Analytix Pvt, Ltd, United States, 62356176, http://www.freepatentsonline.com/y2018/0000461.html
Keith L., Oleszczuk J., Laguens M., Are Mammography and Palpation Sufficient for Breast Cancer Screening? A Dissenting Opinion. J. Women’s Health Gender-Based Med. 11(1):17–25, 2002. https://doi.org/10.1089/152460902753473417
Omranipour R., Kazemian A., Alipour S., Najafi M., Alidoosti M., Navid M., Alikhassi A., Ahmadinejad N., Bagheri K., Izadi S., Comparison of the Accuracy of Thermography and Mammography in the Detection of Breast Cancer. Breast Care 11(4):260–264, 2016. https://doi.org/10.1159/000448347
Yao X., Wei W., Li J., Wang L., Xu Z., Wan Y., Li K., Sun S., A comparison of mammography, ultrasonography, and far-infrared thermography with pathological results in screening and early diagnosis of breast cancer. Asian Biomed. 8(1):11–19, 2014. https://doi.org/10.5372/1905-7415.0801.257
Arora N., Martins D., Ruggerio D., Tousimis E.A., Swistel A.J., Osborne M.P., Simmons R.M., Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Ame. J. Surg. 196(4):523–526, 2008. https://doi.org/10.1016/j.amjsurg.2008.06.015
The Sentinel BreastScan, Medgadget, 15-Jun-2006. Available at: https://www.medgadget.com/2006/06/sentinel_breast_1.html [Accessed 14 Dec. 2019]
Cyrcadia Health — Early Detection Technology for Breast Cancer. Available at: http://cyrcadiahealth.com/ [Accessed 29 Dec. 2019].
Ekici S., Jawzal H., (2020) Breast cancer diagnosis using thermography and convolutional neural networks. Med. Hypothes. 137 https://doi.org/10.1016/j.mehy.2019.109542
Hakim A., Awale R.N., Detection of breast pathology using thermography as a screening tool. In: 15th Quantitative InfraRed Thermography Conference, 2020. [accepted for publication]
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Hakim, A., Awale, R.N. Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review. J Med Syst 44, 136 (2020). https://doi.org/10.1007/s10916-020-01581-y
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DOI: https://doi.org/10.1007/s10916-020-01581-y