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Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

  • Image & Signal Processing
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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

  1. Breast Cancer India : Pink Indian Statistics. Available at: http://www.breastcancerindia.net/statistics/stat_global.html [Accessed 14 Apr. 2019].

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  PubMed  CAS  Google Scholar 

  5. 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

  6. 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

    Article  Google Scholar 

  7. DMR-IR. Available at: http://visual.ic.uff.br/dmi [online] [Accessed 16 Apr. 2019].

  8. 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

    Article  Google Scholar 

  9. 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.]

  10. 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

    Article  PubMed  CAS  Google Scholar 

  11. Gamagami P. (1996) Indirect Signs of Breast Cancer : Angiogenesis study, Atlas of Mammography, Blackwell Science, Cambridge

  12. 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

    Article  PubMed  CAS  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. M/s Tuscano Systems Pvt Ltd: Mammary rotational infrared thermographic system [MAMRIT] PCT/IN 2012/000778 (2012)

  15. Joseph D., Bronzino, 3rd edition. Boca Raton: CRC Press, 2006

    Google Scholar 

  16. 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

    Article  PubMed  CAS  Google Scholar 

  17. Keith L., Oleszczuk J., Laguens M., Circadian rhythm chaos: a new breast cancer marker. Int. J. Fert. Women’s Med. 46: 238–247, 2001

    CAS  Google Scholar 

  18. 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.

  19. 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

    Article  Google Scholar 

  20. 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

  21. 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

    Article  Google Scholar 

  22. Venkataramani K., Jabbireddy S., Madhu H.J., Kakileti S.T. (2017) US Patent Application No. 9,865,052

  23. NoTouch BreastScan [Online] Available at: http://www.notouchbreastscan.com/index.html [Accessed 8 Apr. 2019]

  24. 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

    Article  CAS  Google Scholar 

  25. 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

    Google Scholar 

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. 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

    Google Scholar 

  36. Zare I., Evaluating the thermal imaging system in detecting certain types of breast tissue masses. Biomed. Res. India 27: 670–675, 2016

    Google Scholar 

  37. 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

    Google Scholar 

  38. Hankare P., Shah K., Nair D., Nair D., Breast cancer detection using thermography. Int. Res. J. Eng. Technol. (IRJET). 3 (4): 1061–1064, 2016

    Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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

    Article  CAS  Google Scholar 

  41. 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

  42. 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

    Article  Google Scholar 

  43. 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

  44. 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

  45. 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

  46. 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

    Article  Google Scholar 

  47. 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

    Article  Google Scholar 

  48. 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

    Google Scholar 

  49. 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

  50. 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

    Article  Google Scholar 

  51. 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

    Article  PubMed  CAS  Google Scholar 

  52. 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

    Article  Google Scholar 

  53. 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

    Article  PubMed  PubMed Central  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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

    Article  Google Scholar 

  56. 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

  57. 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

    Google Scholar 

  58. 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

  59. 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

    Article  PubMed  Google Scholar 

  60. 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

    Article  Google Scholar 

  61. 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

    Article  Google Scholar 

  62. 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

    Article  PubMed  CAS  Google Scholar 

  63. 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

    Article  PubMed  CAS  Google Scholar 

  64. 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

  65. 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

    Article  Google Scholar 

  66. 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

    Article  Google Scholar 

  67. 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

    Article  Google Scholar 

  68. 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

    Article  Google Scholar 

  69. 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

    Article  Google Scholar 

  70. 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

    Article  Google Scholar 

  71. 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

    Article  Google Scholar 

  72. 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

    Article  Google Scholar 

  73. 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

    Article  PubMed  CAS  Google Scholar 

  74. 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

  75. 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

  76. 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

    Article  Google Scholar 

  77. Mohamed N.A., Breast cancer risk detection using digital infrared thermal images. Int. J. Bioinform. Biomed. Eng. 1(2):185–194, 2015

    Google Scholar 

  78. 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

    Article  CAS  Google Scholar 

  79. 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

    Article  Google Scholar 

  80. 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

    Article  Google Scholar 

  81. 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

    Article  Google Scholar 

  82. 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

    Article  PubMed  Google Scholar 

  83. 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

    Article  Google Scholar 

  84. 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

    Article  Google Scholar 

  85. 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

    Article  PubMed  CAS  Google Scholar 

  86. 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

    Article  Google Scholar 

  87. 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

    Article  Google Scholar 

  88. 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

    Article  Google Scholar 

  89. 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

  90. 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

  91. 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

    Article  Google Scholar 

  92. 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

    Article  PubMed  Google Scholar 

  93. 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

  94. 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

    Article  Google Scholar 

  95. 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

  96. 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

  97. 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

    Article  Google Scholar 

  98. 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

    Article  Google Scholar 

  99. 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

    Article  Google Scholar 

  100. 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

    Article  Google Scholar 

  101. 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

    Article  CAS  Google Scholar 

  102. 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

    Article  CAS  Google Scholar 

  103. 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

    Book  Google Scholar 

  104. 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

    Article  Google Scholar 

  105. 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

    Article  Google Scholar 

  106. 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

    CAS  Google Scholar 

  107. 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

  108. Venkataramani K. (2016) Detecting tumorous breast tissue in a thermal image, Niramai Health Analytix Pvt. Ltd, US9486146B. https://patents.google.com/patent/US9486146/en

  109. 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

  110. Danicic A., (2016) Methods for thermal breast cancer detection, United States, WO2017184201A1. https://patents.google.com/patent/WO2017184201A1/en

  111. 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

  112. Kakileti S.T., (2018) Blood vessel extraction in two-dimensional thermography, United States, 62356238, http//:shorturl.at/cRWX4

  113. 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

  114. 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

    Article  Google Scholar 

  115. 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

    Article  PubMed  PubMed Central  Google Scholar 

  116. 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

    Article  Google Scholar 

  117. 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

    Article  Google Scholar 

  118. The Sentinel BreastScan, Medgadget, 15-Jun-2006. Available at: https://www.medgadget.com/2006/06/sentinel_breast_1.html [Accessed 14 Dec. 2019]

  119. Cyrcadia Health — Early Detection Technology for Breast Cancer. Available at: http://cyrcadiahealth.com/ [Accessed 29 Dec. 2019].

  120. 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

  121. 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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