Image Processing Techniques for ROI Identification in Rheumatoid Arthritis Patients from Thermal Images

Wasilewska, Agnieszka; Pauk, Jolanta; Ihnatouski, Mikhail

doi:10.2478/ama-2018-0008

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune disease that manifests itself by joint inflammation, swelling, pain, tenderness and may involve extra-articular organs in severe cases. Joint inflammatory lesions are associated with higher temperature due to increased vascularity in the area of inflamed tissues. This papers aimed to identify heat patterns from ROIs to interpret the presence of inflammation in rheumatoid arthritis patients. The thermovisual image sequences were collected from 65 patients with Rheumatoid Arthritis (RA). Infrared images were generated by a thermal scanning camera (FLIR E60bx Systems Inc., USA). Separate recordings of left and right foot temperature changes were performed for 3 minute periods. The temperature measurement was performed at the moment right after cold water immersion (post-cooling temperature) and at the moment after thermal recovery (post-recovery temperature). The recording of 3-minute foot thermal recovery was used for analysis. Automatically identified ROI corresponds to the area of the soft tissues covering cuboid and navicular bone.

References

1. Afshar S., Sheehan M. (2017), Applying infrared thermography and image analysis to dilute 2-phase particulate systems: Hot Particle Curtains, Energy Procedia, 110, 408-413.10.1016/j.egypro.2017.03.161
Search in Google Scholar Back to article
2. Barcelos Z.E., Caminhas W.M., Ribeiro E., Pimenta E.M., Palhares R.M. (2014), A combined method for segmentation and registration for an advanced and progressive evaluation of thermal images, Sensors, 14, 21950-21967.10.3390/s141121950
Open DOI Search in Google Scholar Back to article
3. Bezerra H.G., Attizzani G.F., Sirbu V., Musumeci G., Lortkipanidze N., Fujino Y., Wang W., Nakamura S., Erglis A., Guagliumi G., Costa M.A. (2013), Optical coherence tomography versus intravascular ultrasound to evaluate coronary artery disease and percutaneous coronary intervention, JACC Cardiovasc Interv., 6(3), 228-36.10.1016/j.jcin.2012.09.017
Search in Google Scholar Back to article
4. Cojocaru M., Cojocaru I.M., Silosi I., Vrabie C.D., Tanasescub R., (2010), Extra-articular manifestations in rheumatoid arthritis; Maedica (Buchar), 5(4), 286–291.
Search in Google Scholar Back to article
5. Dey N., Ashour A.S., Chakraborty S., Banerjee S., Gospodinova E., Gospodinov M., Hassanien A.E. (2017), Watermarking in biomedical signal processing, Intelligent Techniques in Signal Processing for Multimedia Security, Dey N, Santhi V (eds); Springer International Publishing, 345-369.10.1007/978-3-319-44790-2_16
Search in Google Scholar Back to article
6. Fernández-Cuevas I., Bouzas Marins J.C., Arnáiz Lastras J., Gómez Carmona P.M., Piñonosa Cano S., García-Concepción M.A., Sillero-Quintana M. (2015), Classification of factors influencing the use of infrared thermography in humans: a review, Infrared Physics & Technology 71, 28–55.10.1016/j.infrared.2015.02.007
Search in Google Scholar Back to article
7. Gabriel S.E. (2001), The epidemiology of rheumatoid arthritis, Rheum Dis Clin North Am., 27(2), 269–81.10.1016/S0889-857X(05)70201-5
Open DOI Search in Google Scholar Back to article
8. Herman C., Pirtini Cetingul M. (2011, Quantitative visualization and detection of skin cancer using dynamic thermal imaging, J. Vis. Exp., 51, 1-4.10.3791/2679319710821587160
Search in Google Scholar Back to article
9. Ihnatouski M.I. (2000), Methods of segmentation of AFM and STM images. Recognition and description of cluster surface objects in the nanoscale IV Belarusian seminar on scanning probe microscopy (SPM-4). V.A. Belyi Metal Polymer Research Institute: 122-126.
Search in Google Scholar Back to article
10. John H.A., Niumsawatt V., Rozen W.M., Whitaker I.S. (2016), Clinical applications of dynamic infrared thermography in plastic surgery: a systematic review, Gland Surg, 5(2), 122-132.
Search in Google Scholar Back to article
11. Jones B.F. (1998), A re-appraisal of the use of infrared thermal image analysis in medicine, IEEE Trans Med Imaging, 17,1019–27.10.1109/42.74663510048859
Open DOI Search in Google Scholar Back to article
12. Kaczmarek M., Nowakowski A. (2016), Active IR-thermal imaging in medicine, J Nondestruct Eval, 35(19), 1-16.10.1007/s10921-016-0335-y
Open DOI Search in Google Scholar Back to article
13. Kovalev V., Petrou M. (1996), Multidimensional co-occurrence matrices for object recognition and matching, Graphical Models and Image Processing., 58(3), 187-197.10.1006/gmip.1996.0016
Search in Google Scholar Back to article
14. Lahiri B.B., Bagavathiappan S., Jayakumar T. (2012), Medical applications of infrared thermography, A review, Infrared Physics & Technology, 55(4), 221-235.10.1016/j.infrared.2012.03.007711078732288544
Open DOI Search in Google Scholar Back to article
15. Ludwig N., Formenti D., Gargano M., Alberti G. (2014), Skin temperature evaluation by infrared thermography: comparison of image analysis methods, Infrared Physics & Technology, 62,1-6.10.1016/j.infrared.2013.09.011
Open DOI Search in Google Scholar Back to article
16. Nowakowski A., Siondalski P., Moderhak M., Kaczmarek M. (2014), Problems of cardiosurgery wound healing evaluation, Proceedings of QIRT, 1–9.
Search in Google Scholar Back to article
17. Nowakowski A., Siondalski P., Moderhak M., Kaczmarek M. (2015), A new diagnostic method for evaluation of cardiosurgery wound healing, JQIRT, 1–16.10.1080/17686733.2015.1077543
Search in Google Scholar Back to article
18. Purslow C., Wolffsohn J.S., Santodomingo-Rubido J. (2005), The effect of contact lens wear on dynamic ocular surface temperature, Contact Lens & Anterior Eye, 28, 29–36.10.1016/j.clae.2004.10.00116318832
Search in Google Scholar Back to article
19. Renkielska A., Kaczmarek M., Nowakowski A., Grudzinski J., Czapiewski P., Krajewski A., Grobelny I. (2014), Active dynamic infrared thermal imaging in burn depth evaluation, J. Burn Care Res, 35(5), 294–303.10.1097/BCR.000000000000005925144810
Search in Google Scholar Back to article
20. Renkielska A., Nowakowski A., Kaczmarek M., Ruminski J. (2006), Burn depths evaluation based on active dynamic IR thermal imaging—a preliminary study, Burns, 32, 867–875.10.1016/j.burns.2006.01.02416997482
Open DOI Search in Google Scholar Back to article
21. Ring E.F.J. (1998), Progress in the measurement of human body temperature, IEEE Eng Med Biol, 17, 19–24.10.1109/51.6879599672806
Search in Google Scholar Back to article
22. Tattersall G.J. (2016), Infrared thermography: non-invasive window into thermal physiology, Comp Biochem Physiol A Mol Integr Physiol, 202, 78-98.10.1016/j.cbpa.2016.02.02226945597
Search in Google Scholar Back to article
23. Wasilewska A. (2017), Advantages of active over passive thermography in terms of applying in medicine, Scientific and didactic equipment, 22(2), 88-93.
Search in Google Scholar Back to article
24. Wasilewska A., Pauk J. (2017), Safety conditions in dynamic IT examinations of rheumatoid arthritis lesions, Scientific and didactic equipment, 22(3), 205-214.
Search in Google Scholar Back to article

Image Processing Techniques for ROI Identification in Rheumatoid Arthritis Patients from Thermal Images

Abstract

Paradigm

My account