Tissue Mimicking Phantom Design and Characterization for Thermal Imaging Applications on Medical Diagnosis
Yıl 2023,
Cilt: 19 Sayı: 1, 31 - 37, 28.03.2023
Zeynep Ayyıldız
,
İbrahim Akkaya
,
Mehmet Engin
Öz
Breast cancer is one of the mortal cancerous for women and an early diagnosis, applying an appropriate treatment and prognosis increases the survival chance of the patients. There are different screening methods and thermal imaging is one of the noninvasive promising diagnosis techniques to detect thermal profile anomalies in breasts. This work includes both simulation and experimental studies for the detection of breast tumors by using thermal images. The first step is the simulation studies based on heat transfer in biological tissues. By using the Bio-Heat transfer theory, temperature differences between the healthy and tumorous tissues are acquired. The second step consists of phantom designs and detection of breast tumor via thermographic imaging in in-vitro. Designing an appropriate phantom is tremendously crucial for the calibration of the thermal imaging system and diagnosis of breast cancer. As a result of the study, it is presented that the detection of temperatures difference especially with asymmetry factor between the tumor and healthy tissue region is feasible. Also, it is shown that the simulation based results are consistent with the experimental as well.
Destekleyen Kurum
Ege Üniversitesi, Bilimsel Araştırma Destekleme Birimi
Proje Numarası
ONAP-FOA-2020-22288.
Teşekkür
This study was supported by Ege University grant no. ONAP-FOA-2020-22288.
Kaynakça
- Terrasse V. 2020. Latest global cancer data, Report No: 292, International Agency for Research on Cancer (IARC), 2020.
- Lin Q. Y., Yang H. Q., Xie S. S., Wang Y. H., Ye Z., Chrn S. Q. 2009. Detecting early breast tumor by finite element thermal analysis, J. of Med. Eng. And Tech., 33(4): 274-280.
- Hernandez J. L. G., Recinella A. N., Kandlikar S. G., Dabydeen D., Medeiros L., Phatak P. 2019, Technology, application and potential of dynamic breast thermography for the detection of breast cancer, Int. J. of Heat and Mass Transfer, 131: 558-573.
- Levy A., Dayan A., Ben-David M., Gannot I. 2010, A new thermography-based approach to early detection of cancer utilizing magnetic nanoparticles theory simulation and in-vitro validation, Nanomedicine: Nanotech., Bio., and Med., 6(6), 786 – 769.
- Wahab A. A., Salim M. I. M., Yunus J., Thermal distrubition analysis in multi-layers homogenous phantom using infrared technique: A preliminary study, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014, 628-633.
- Hossain S., Mohammadi F., One-dimensional steady state analysis of bioheat transfer equation: tumor parameters assessment for medical diagnosis application, Proceedings 6th international multi-conference on engineering and technological innovation (IMETI 2013), 2013, pp 26–30.
- Roslidar, R., Rahman, A., Muharar, R., Syahputra, M. R., Arnia, F., Syukri, M., Pradhan, B., & Munadi, K. 2020. A review on recent progress in thermal ımaging and deep learning approaches for breast cancer detection, IEEE Access, 8: 116176 – 116194.
- Galvan J. C. T., Guevara E., Machuca E. S. K., Villanueva A. O., Flores J. L., Gonzalez F. J. 2021. Deep convolutional neural networks for classifying breast cancer using infrared thermography, Quantitative Infrared Thermography Journal, DOI: 10.1080/17686733.2021.1918514.
- Allugunti V. R. 2022. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms, Int. J. of Eng. in Comp. Sci., 4(1): 49-56.
- Resmini R., Silva L., Araujo A. S., Medeiros P., Saade D. M., Conci A. 2021. Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography, Sensors, 21(14), 4802.
- Bezerra L. A., Oliveira M. M.,Rolim T. L., Conci A., Santos F. G. S., Lyra P. R. M., Lima R. C. F. 2013. Estimation of breast tumor thermal properties using infrared images, Signal Processing, 93: 2851-2863.
- Wang H., Qin Q.H. Computational bioheat modeling in human eye with local blood perfusion effect, Human Eye Imaging and Modeling, 1st edn. CRC Press, 2012, pp. 311-28.
- Xu F., Lu T. J., Seffen K. A. 2008. Biothermomechanics of skin tissue, J. of the Mechanics and Physics, 56(5):1852-1884.
- Gonzales F. J. 2007. Thermal simulation of breast tumors, Revista Mexicana de Fisica, 53(4): 323-326.
- Pennes, HH. 1948. Analysis of tissue and arterial blood temperatures in the resting human forearm. J. of Applied Physiology, 1(2): 93-122.
- Fung Y. C., Biomechanics: Motion, Flow, Stress, and Growth, Springer, New York, USA, 1990.
Yıl 2023,
Cilt: 19 Sayı: 1, 31 - 37, 28.03.2023
Zeynep Ayyıldız
,
İbrahim Akkaya
,
Mehmet Engin
Proje Numarası
ONAP-FOA-2020-22288.
Kaynakça
- Terrasse V. 2020. Latest global cancer data, Report No: 292, International Agency for Research on Cancer (IARC), 2020.
- Lin Q. Y., Yang H. Q., Xie S. S., Wang Y. H., Ye Z., Chrn S. Q. 2009. Detecting early breast tumor by finite element thermal analysis, J. of Med. Eng. And Tech., 33(4): 274-280.
- Hernandez J. L. G., Recinella A. N., Kandlikar S. G., Dabydeen D., Medeiros L., Phatak P. 2019, Technology, application and potential of dynamic breast thermography for the detection of breast cancer, Int. J. of Heat and Mass Transfer, 131: 558-573.
- Levy A., Dayan A., Ben-David M., Gannot I. 2010, A new thermography-based approach to early detection of cancer utilizing magnetic nanoparticles theory simulation and in-vitro validation, Nanomedicine: Nanotech., Bio., and Med., 6(6), 786 – 769.
- Wahab A. A., Salim M. I. M., Yunus J., Thermal distrubition analysis in multi-layers homogenous phantom using infrared technique: A preliminary study, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014, 628-633.
- Hossain S., Mohammadi F., One-dimensional steady state analysis of bioheat transfer equation: tumor parameters assessment for medical diagnosis application, Proceedings 6th international multi-conference on engineering and technological innovation (IMETI 2013), 2013, pp 26–30.
- Roslidar, R., Rahman, A., Muharar, R., Syahputra, M. R., Arnia, F., Syukri, M., Pradhan, B., & Munadi, K. 2020. A review on recent progress in thermal ımaging and deep learning approaches for breast cancer detection, IEEE Access, 8: 116176 – 116194.
- Galvan J. C. T., Guevara E., Machuca E. S. K., Villanueva A. O., Flores J. L., Gonzalez F. J. 2021. Deep convolutional neural networks for classifying breast cancer using infrared thermography, Quantitative Infrared Thermography Journal, DOI: 10.1080/17686733.2021.1918514.
- Allugunti V. R. 2022. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms, Int. J. of Eng. in Comp. Sci., 4(1): 49-56.
- Resmini R., Silva L., Araujo A. S., Medeiros P., Saade D. M., Conci A. 2021. Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography, Sensors, 21(14), 4802.
- Bezerra L. A., Oliveira M. M.,Rolim T. L., Conci A., Santos F. G. S., Lyra P. R. M., Lima R. C. F. 2013. Estimation of breast tumor thermal properties using infrared images, Signal Processing, 93: 2851-2863.
- Wang H., Qin Q.H. Computational bioheat modeling in human eye with local blood perfusion effect, Human Eye Imaging and Modeling, 1st edn. CRC Press, 2012, pp. 311-28.
- Xu F., Lu T. J., Seffen K. A. 2008. Biothermomechanics of skin tissue, J. of the Mechanics and Physics, 56(5):1852-1884.
- Gonzales F. J. 2007. Thermal simulation of breast tumors, Revista Mexicana de Fisica, 53(4): 323-326.
- Pennes, HH. 1948. Analysis of tissue and arterial blood temperatures in the resting human forearm. J. of Applied Physiology, 1(2): 93-122.
- Fung Y. C., Biomechanics: Motion, Flow, Stress, and Growth, Springer, New York, USA, 1990.