Singular value based characterization and analysis of thermal patches for early breast abnormality detection

  • Usha Rani GogoiEmail author
  • Mrinal Kanti Bhowmik
  • Debotosh Bhattacharjee
  • Anjan Kumar Ghosh
Scientific Paper


The purpose of this study is to develop a novel breast abnormality detection system by utilizing the potential of infrared breast thermography (IBT) in early breast abnormality detection. Since the temperature distributions are different in normal and abnormal thermograms and hot thermal patches are visible in abnormal thermograms, the abnormal thermograms possess more complex information than the normal thermograms. Here, the proposed method exploits the presence of hot thermal patches and vascular changes by using the power law transformation for pre-processing and singular value decomposition to characterize the thermal patches. The extracted singular values are found to be statistically significant (p < 0.001) in breast abnormality detection. The discriminability of the singular values is evaluated by using seven different classifiers incorporating tenfold cross-validations, where the thermograms of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) and Database of Mastology Research (DMR) databases are used. In DMR database, the highest classification accuracy of 98.00% with the area under the ROC curve (AUC) of 0.9862 is achieved with the support vector machine using polynomial kernel. The same for the DBT-TU-JU database is 92.50% with AUC of 0.9680 using the same classifier. The comparison of the proposed method with the other reported methods concludes that the proposed method outperforms the other existing methods as well as other traditional feature sets used in IBT based breast abnormality detection. Moreover, by using Rank1 and Rank2 singular values, a breast abnormality grading (BAG) index has also been developed for grading the thermograms based on their degree of abnormality.


Breast cancer Infrared breast thermography Thermal patches Singular value decomposition Breast abnormality detection Breast abnormality grading 



The work presented here is being conducted in the Bio-Medical Infrared Image Processing Laboratory (BMIRD) of Computer Science and Engineering Department, Tripura University (A Central University), Tripura (W). The first author is grateful to Department of Science and Technology (DST), Government of India for providing her Junior Research Fellowship (JRF) under DST INSPIRE fellowship program (No. IF150970). The authors would also like to thank Dr. Gautam Majumdar, Regional Cancer Centre, Agartala Govt. Medical College for his kind support to carry out this work.


This study was funded by Department of Biotechnology (DBT), Government of India (Grant No. BT/533/NE/TBP/2013, Dated 03/03/2014).

Compliance with ethical standards

Conflict of interest

All authors declare that they don’t have any conflict of interest.

Ethical approval

This work is done by maintaining the ethical standards of AGMC with IRB approval number F.4 (5-2)/AGMC/Academic/ Project/Research/2007/Sub-I/ 8199-8201.


  1. 1.
    Statistics of Breast Cancer in India. Accessed 30 Aug 2017
  2. 2.
    Ng EYK (2009) A review of thermography as promising non-invasive detection modality for breast tumor. Int J Therm Sci 48(5):849–859CrossRefGoogle Scholar
  3. 3.
    Gogoi UR, Majumdar G, Bhowmik MK, Ghosh AK, Bhattacharjee D, Breast abnormality detection through statistical feature analysis using infrared thermograms. In: Proceeding of IEEE international symposium on advanced computing and communication (ISACC), 2015, pp 258–265Google Scholar
  4. 4.
    Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J (2012) Medical applications of infrared thermography: a review. Infrared Phys Technol 55(4):221–235CrossRefGoogle Scholar
  5. 5.
    Schaefer G, Závišek M, Nakashima T (2009) Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn 42(6):1133–1137CrossRefGoogle Scholar
  6. 6.
    Faria FAC, Cano SP, Gomez-Carmona PM, Sillero M, Neiva CM (2012) Infrared thermography to quantify the risk of breast cancer. Bioimages 20(0):1–7Google Scholar
  7. 7.
    Uematsu S (1985) Symmetry of skin temperature comparing one side of the body to the other. Thermology 1(1):4–7Google Scholar
  8. 8.
    Kwok J, Krzyspiak J (2007) Thermal imaging and analysis for breast tumor detection. Computer-aided engineering: applications to biomedical processes, BEE 453Google Scholar
  9. 9.
    Rastgar-Jazi M, Mohammadi F (2017) Parameters sensitivity assessment and heat source localization using infrared imaging techniques. Biomed Eng Online 16(1):113CrossRefGoogle Scholar
  10. 10.
    Qi H, Snyder WE, Head JF, Elliott RL (2000) Detecting breast cancer from infrared images by asymmetry analysis. In: Proceedings of the 22nd IEEE annual international conference of the IEEE Engineering in Medicine and Biology Society, vol 2, pp 1227–1228Google Scholar
  11. 11.
    Borchartt TB, Conci A, Lima RCF, Resmini R, Sanchez A (2013) Breast thermography from an image processing viewpoint: a survey. Signal Process 93(10):2785–2803CrossRefGoogle Scholar
  12. 12.
    Gogoi UR, Bhowmik MK, Bhattacharjee D, Ghosh AK, Majumdar G (2016) A study and analysis of hybrid intelligent techniques for breast cancer detection using breast thermograms. In: Bhattacharyya S, Dutta P, Chakraborty S (eds) Hybrid soft computing approaches. Springer, New Delhi, pp 329–359CrossRefGoogle Scholar
  13. 13.
    Bhowmik MK, Gogoi UR, Majumdar G, Datta D, Ghosh AK, Bhattacharjee D (2018) Designing of ground truth annotated DBT-TU-JU breast thermogram database towards early abnormality prediction. IEEE J Biomed Health Inform (JBHI) 22(4):1238–1249CrossRefGoogle Scholar
  14. 14.
    Silva LF, Saade DCM, Sequeiros-Olivera Silva GO, Paiva AC, Bravo RS, Conci A (2014) A new database for breast research with infrared image. J Med Imaging Health Inform 4(1):92–100 9CrossRefGoogle Scholar
  15. 15.
    Ng EYK, Kee EC (2007) Integrative computer-aided diagnostic with breast thermogram. J Mech Med Biol 7(01):1–10CrossRefGoogle Scholar
  16. 16.
    Mookiah MRK, Acharya UR, Ng EYK (2012) Data mining technique for breast cancer detection in thermograms using hybrid feature extraction strategy. Quant Infrared Thermogr J 9(2):151–165CrossRefGoogle Scholar
  17. 17.
    Acharya UR, Ng EYK, Tan JH, Sree SV (2012) Thermography based breast cancer detection using texture features and support vector machine. J Med Syst 36(3):1503–1510CrossRefGoogle Scholar
  18. 18.
    Francis SV, Sasikala M (2013) Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features. J Med Eng Technol 37(1):17–21CrossRefGoogle Scholar
  19. 19.
    Francis SV, Sasikala M, Bharathi GB, Jaipurkar SD (2014) Breast cancer detection in rotational thermography images using texture features. Infrared Phys Technol 67:490–496CrossRefGoogle Scholar
  20. 20.
    Francis SV, Sasikala M, Saranya S (2014) Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J Med Syst 38(4):23CrossRefGoogle Scholar
  21. 21.
    Araújo MC, Lima RC, De Souza RM (2014) Interval symbolic feature extraction for thermography breast cancer detection. Expert Syst Appl 41(15):6728–6737CrossRefGoogle Scholar
  22. 22.
    Garduño-Ramón MA, Vega-Mancilla SG, Morales-Henández LA, Osornio-Rios RA (2017) Supportive noninvasive tool for the diagnosis of breast cancer using a thermographic camera as sensor. Sensors 17(3):497CrossRefGoogle Scholar
  23. 23.
    Gaber T, Ismail G, Anter A, Soliman M, Ali M, Semary N, Snasel V (2015) Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm. In: 37th IEEE annual international conference in Engineering in Medicine and Biology Society (EMBC), pp 4254–4257Google Scholar
  24. 24.
    Zadeh HG, Haddadnia J, Hashemian M, Hassanpour K (2012) Diagnosis of breast cancerusing a combination of genetic algorithm and artificial neural network in medical infrared thermal imaging. Iran J Med Phys 9(4):265–274Google Scholar
  25. 25.
    Sathish D, Kamath S, Prasad K, Kadavigere R, Martis RJ (2017) Asymmetry analysis of breast thermograms using automated segmentation and texture features. Signal Image Video Process 11(4):745–752CrossRefGoogle Scholar
  26. 26.
    Borchartt TB, Resmini R, Conci A, Martins A, Silva AC, Diniz EM, Lima RC (2011) Thermal feature analysis to aid on breast disease diagnosis. In: Proceedings of 21st Brazilian Congress of Mechanical Engineering—COBEM2011, pp 24–28Google Scholar
  27. 27.
    Lashkari A, Pak F, Firouzmand M (2016) Full intelligent cancer classification of thermal breast images to assist physician in clinical diagnostic applications. J Med Signals Sens 6(1):12PubMedPubMedCentralGoogle Scholar
  28. 28.
    Suganthi SS, Ramakrishnan S (2014) Semi-automatic segmentation of breast thermograms using variational level set method. In: Proceedings of 15th international conference on biomedical engineering, Springer International Publishing, Singapore, pp 231–234Google Scholar
  29. 29.
    Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios. Recommendation ITU-R BT.601-7, 2011Google Scholar
  30. 30.
    Luo H, Lin D, Yu C, Chen L (2013) Application of different HSI color models to detect fire-damaged mortar. Int J Transp Sci Technology 2(4):303–316CrossRefGoogle Scholar
  31. 31.
    Gonzalez RC, Woods RF (1992) Digital image processing. Addison Wesley, ReadingGoogle Scholar
  32. 32.
  33. 33.
    Petersen KB, Pedersen MS (2008) The matrix cookbook, vol. 7, no. 15. Technical University of Denmark, Kongens Lyngby, pp 510Google Scholar
  34. 34.
    Matrix Norms and Condition Numbers. Accessed 11 April 2018
  35. 35.
    Veisi H, Jamzad M (2009) A complexity-based approach in image compression using neural networks. Int J Signal Process 5(2):82–92Google Scholar
  36. 36.
    Yu H, Stefan W (2013) Image complexity and spatial information. In: Quality of Multimedia Experience (QoMEX), fifth international workshop on Klagenfurt am Wörthersee, IEEE, pp 12–17Google Scholar
  37. 37.
    Backes AR, Bruno OM (2010) Medical image retrieval based on complexity analysis. Mach Vision Appl 21(3):217–227CrossRefGoogle Scholar
  38. 38.
    Falconer K (2004) Fractal geometry: mathematical foundations and applications. Wiley, New YorkGoogle Scholar
  39. 39.
    Fisher RA (1934) Statistical methods for research workers, 13th edn. Hafner, New YorkGoogle Scholar
  40. 40.
    Gogoi UR, Bhowmik MK, Ghosh AK, Bhattacharjee D, Majumdar G (2017) Discriminative feature selection for breast abnormality detection and accurate classification of thermograms. In: Proceeding of IEEE international conference on innovations in electronics, signal processing and communication (IESC), pp 39–44Google Scholar
  41. 41.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1(6):80–83CrossRefGoogle Scholar
  42. 42.
    Karimollah HT (2013) Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 4(2):627–635Google Scholar
  43. 43.
    Mandrekar JN (2010) Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5(9):1315–1316CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringTripura University (A Central University)SuryamaninagarIndia
  2. 2.Department of Computer Science & EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations