Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery

  • Kakali DasEmail author
  • Mrinal Kanti Bhowmik
  • Omkar Chowdhuary
  • Debotosh Bhattacharjee
  • Barin Kumar De
Technical Paper


Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.


Hotspot detection Inflammation Region growing Thermal imaging 



The research work was supported by the Grant No. 5/7/1516/2016-RCH Dated: 20/06/2017 from the Indian Council of Medical Research (ICMR), Government of India.

Compliance with ethical standards

Conflict of interest

The authors declare there is no potential conflict of interestwith respect to the authorship and/or publication of this article.

Ethical approval

Ref. No.4(6-11)-AGMC/Medical Education/Ethics Com/2018/15136, Dated 31st December, 2018.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Lee J, Lee J, Song S, Lee H, Lee K, Yoon Y (2008) Detection of suspicious pain regions on a digital infrared thermal image usingthe multimodal function optimization. In: 30th annual international conference of the IEEE engineering in medicine and biology society 2008Google Scholar
  2. 2.
    Nola IA, Kolanc D (2015) Thermography in biomedicine. In: 57th international symposium ELMAR (ELMAR)Google Scholar
  3. 3.
    Gogoi UR, Bhowmik MK, Bhattacharjee D, Ghosh AK, Majumdar G (2015) A study and analysis of hybrid intelligent techniques for breast cancer detection using breast thermograms. In: Dutta P, Chakraborty S, Bhattacharyya S (eds) Hybrid soft computing approaches studies in computational intelligence. Springer, New Delhi, pp 329–359Google Scholar
  4. 4.
    Brioschi M, Teixeira ML, Silva MT, Colman FM (2010) Medical thermography textbook: principles and applications. Andreoli, Sao PauloGoogle Scholar
  5. 5.
    Qi H, Kuruganti PT, Snyder WE (2012) Detecting breast cancer from thermal infrared images by asymmetry analysis. In: Medicine and Medical Research, vol 38Google Scholar
  6. 6.
    Liu C, Heijden FVD, Klein ME, Baal JGV, Bus SA, Netten JJV (2013) Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study. In: Mahadevanj-Jansen A, Vo-Dinh T, Grundfest WS (eds) Advanced Biomedical and Clinical Diagnostic Systems. SPIE, Bellingham, p 9Google Scholar
  7. 7.
    Ring F (2010) Thermal imaging today and its relevance to diabetes. J Diabetes Sci Technol 4(4):857–862CrossRefGoogle Scholar
  8. 8.
    Ring EFJ (1978) Thermographic terminology. Acta Thermo-Grap 2:1–30Google Scholar
  9. 9.
    Jones B (1998) A reappraisal of the use of infrared thermal image analysis in medicine. IEEE Trans Med Imaging 17:1019–1027CrossRefGoogle Scholar
  10. 10.
    González RC, Woods RE (2002) Digital image processing. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  11. 11.
    Palmer M (2016) TH-E-209-00: radiation dose monitoring and protocol management. Med Phys 43:3902CrossRefGoogle Scholar
  12. 12.
    Takeda T (2016) Treatment strategy of elderly rheumatoid arthritis. Jpn J Clin Immunol 39:497–504CrossRefGoogle Scholar
  13. 13.
    Gogoi UR, Majumdar G, Bhowmik MK, Ghosh AK, Bhattacharjee D (2015) Breast abnormality detection through statistical feature analysis using infrared thermograms. In: Proceedings of the international symposium on advanced computing and communication (ISACC) 2015Google Scholar
  14. 14.
    Bardhan S, Bhowmik MK, Nath S, Bhattacharjee D (2015) A review on inflammatory pain detection in human body through infrared image analysis. In: Proccedings of the international symposium on advanced computing and communication (ISACC) 2015Google Scholar
  15. 15.
    Bhowmik MK, Gogoi UR, Majumdar G, Bhattacharjee D, Datta D, Ghosh AK (2017) Designing of ground truth annotated DBT-TU-JU breast thermogram database towards early abnormality prediction. IEEE J Biomed Health Inform 22:1238–1249CrossRefGoogle Scholar
  16. 16.
    Silva LF, Saade DCM, Sequeiros GO, Silva AC, Paiva AC, Bravo RS, Conci A (2014) A new database for breast research with infrared image. J Med Imaging Health Inform 4:92–100CrossRefGoogle Scholar
  17. 17.
    Bhowmik MK, Bardhan S, Das K, Bhattacharjee D, Nath S (2016) Pain related inflammation analysis using infrared images. In: Zalameda JN, Bison P (eds) Thermosense: thermal infrared applications XXXVIII. SPIE, Bellingham, p 14Google Scholar
  18. 18.
    Adams Leanne (1994) Seeded RG. IEEE Trans Pattern Anal Mach Intell 16:641–647CrossRefGoogle Scholar
  19. 19.
    Bhowmik MK, Bardhan S, Das K, Bhattacharjee D, Nath S (2016) Pain related inflammation analysis using infrared images. In: Zalameda JN, Bison P (eds) Thermosense: thermal infrared applications XXXVIII. SPIE, Bellingham, p 14Google Scholar
  20. 20.
    Paulano F, Jiménez JJ, Pulido R (2014) 3D segmentation and labeling of fractured bone from CT images. Vis Comput 30:939–948CrossRefGoogle Scholar
  21. 21.
    Zaproudina N, Varmavuo V, Airaksinen O, Närhi M (2008) Reproducibility of infrared thermography measurements in healthy individuals. Physiol Meas 29:515–524CrossRefGoogle Scholar
  22. 22.
    Denoble AE, Hall N, Pieper CF, Kraus VB (2010) Patellar skin surface temperature by thermography reflects knee osteoarthritis severity. Clin Med Insights 45:78–96. Google Scholar
  23. 23.
    Herry CL, Frize M, Goubran RA (2008) Search for abnormal thermal patterns in clinical thermal infrared imaging. In: IEEE international workshop on medical measurements and applications 2008Google Scholar
  24. 24.
    Leroy P (1995) Update in interventional digitalized infra-red thermal imaging in pain management. In: Proceedings of 17th international conference of the engineering in medicine and biology societyGoogle Scholar
  25. 25.
    Ijzerman RG, Serne EH, Weissenbruch MMV, Jongh RTD, Stehouwer CDA (2003) Cigarette smoking is associated with an acute impairment of microvascular function in humans. Clin Sci 104:247CrossRefGoogle Scholar
  26. 26.
    Steketee J (1973) Spectral emissivity of skin and pericardium. Phys Med Biol 18:686–694CrossRefGoogle Scholar
  27. 27.
    Disasi AF (2003) Early effects of cigarette smoking in hypertensive and normotensive subjects An ambulatory blood pressure and thermographic study. J Min cardioangiol 51:387–393Google Scholar
  28. 28.
  29. 29.
    Herry CL, Frize M, Goubran RA (2008) Search for abnormal thermal patterns in clinical thermal infrared imaging. In: Proceedings of the 2008 IEEE international workshop on medical measurements and applications.
  30. 30.
    Shareef N, Wang D, Yagel R (1999) Segmentation of medical images using LEGION. IEEE Trans Med Imaging 18:74–91CrossRefGoogle Scholar
  31. 31.
    Oliver E, Ruiz J, She S, Wang J (2006) The software architecture of the GIMP, 2006Google Scholar
  32. 32.
    Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM (2017) A bottom-up approach for pancreas segmentation using cascaded superpixels and (Deep) image patch labeling. IEEE Trans Image Process 26:386–399CrossRefGoogle Scholar
  33. 33.
    Li C, Wang X, Eberl S, Fulham M, Yin Y, Chen J, Feng DD (2013) A likelihood and local constraint level set model for liver tumor segmentation from CT volumes. IEEE Trans Biomed Eng 60:2967–2977CrossRefGoogle Scholar
  34. 34.
    Philipsen RHHM, Maduskar P, Hogeweg L, Melendez J, Sanchez CI, Ginneken BV (2015) Localized energy-based normalization of medical images: application to chest radiography. IEEE Trans Med Imaging 34:1965–1975CrossRefGoogle Scholar
  35. 35.
    Chiang J, Birla S, Bedoya M, Jones D, Subbiah J, Brace CL (2015) Modeling and validation of microwave ablations with internal vaporization. IEEE Trans Biomed Eng 62:657–663CrossRefGoogle Scholar
  36. 36.
    Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52:2382–2394CrossRefGoogle Scholar
  37. 37.
    Sadri AR, Zekri M, Sadri S, Gheissari N, Mokhtari M, Kolahdouzan F (2013) Segmentation of dermoscopy images using wavelet networks. IEEE Trans Biomed Eng 60:1134–1141CrossRefGoogle Scholar
  38. 38.
    Bi L, Kim J, Kumar A, Fulham M, Feng D (2017) Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation. Vis Comput 33:1061–1071CrossRefGoogle Scholar
  39. 39.
    Chung F, Schmid J, Magnenat-Thalmann N, Delingette H (2010) Comparison of statistical models performance in case of segmentation using a small amount of training datasets. Vis Comput 27:141–151CrossRefGoogle Scholar
  40. 40.
    6.4 homomorphic filtering using a low pass filter. In: 2.2 Basic Principles of MRI. Accessed 21 Jun 2018
  41. 41.
    Jadin MS, Taib S (2012) Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography. Infrared Phys Technol 55(4):236–245CrossRefGoogle Scholar
  42. 42.
    Etehadtavakol M, Sadri S, Ng EYK (2008) Application of K- and Fuzzy C-means for color segmentation of thermal infrared breast images. J Med Syst 34(1):35–42CrossRefGoogle Scholar
  43. 43.
    Snekhalatha U, Anburajan M, Sowmiya V, Venkatraman B, Menaka M (2015) Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis. Proc Inst Mech Eng Part H J Eng Med 229(4):319–331CrossRefGoogle Scholar
  44. 44.
    Shahari S, Wakankar A (2015) Color analysis of thermograms for breast cancer detection. International Conference on Industrial Instrumentation and Control (ICIC)Google Scholar
  45. 45.
    Etehadtavakol M, Ng E, Lucas C, Sadri S (2012) Fuzzy C means segmentation and fractal analysis of the benign and malignant breast thermograms. In: Medical infrared imaging, pp 1–20Google Scholar
  46. 46.
    Etehadtavakol M, Chandran V, Ng E, Kafieh R (2013) Breast cancer detection from thermal images using bispectral invariant features. Int J Therm Sci 69:21–36CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  • Kakali Das
    • 1
    Email author
  • Mrinal Kanti Bhowmik
    • 1
  • Omkar Chowdhuary
    • 1
  • Debotosh Bhattacharjee
    • 2
  • Barin Kumar De
    • 3
  1. 1.Computer Science and EngineeringTripura UniversitySuryamaninagarIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.Department of PhysicsTripura UniversitySuryamaninagarIndia

Personalised recommendations