TY - GEN
T1 - Algorithm for Automated Segmentation and Feature Extraction of Thermal Images
AU - Poplavska, Anna A.
AU - Vassilenko, Valentina B.
AU - Poplavskyi, Oleksandr A.
AU - Pavlov, Sergei V.
N1 - The authors would like to thank the Fundação para a Ciência e Tecnologia (FCT, Portugal) and NMT, S.A. for co-financing the PhD grant (PD/BDE/142791/2018) of the Doctoral NOVA 14H Program.
Our acknowledgments to Fernando Pimentel Santos from NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM), Chronic Diseases Research Center (CEDOC), for the possibility to perform the measurements during the MyoSpA project. Special thanks to Diogo Casal from NOVA Medical School – NOVA University of Lisbon for assistance in some tasks that need to be solved from the medical point of view.
PY - 2020
Y1 - 2020
N2 - Medical infrared thermal imaging techniques can provide the high-quality images for monitoring and pre-clinical diagnostic of the diseases by showing the thermal abnormalities available in the body. Its biggest advantage is non-contact, non-invasive and very fast way of use. However, the incorrect interpretation of the thermal images via simple observation or manual analysis and detection approximate regions of interest lead to the numerous false positive results. The main objective of this research work is to develop an algorithm for automated image processing and analysis of the extracted features on thermal images for screening or pre-diagnosis of the diseases. This work presents the results of our previously developed Copyright algorithm applied to thermal images obtained from the patients with Axial Spondyloarthritis. This processing and analysis of the extracted features from thermal images can offer a novel quick and non-invasive tool for diagnosis and monitoring of rheumatoid diseases.
AB - Medical infrared thermal imaging techniques can provide the high-quality images for monitoring and pre-clinical diagnostic of the diseases by showing the thermal abnormalities available in the body. Its biggest advantage is non-contact, non-invasive and very fast way of use. However, the incorrect interpretation of the thermal images via simple observation or manual analysis and detection approximate regions of interest lead to the numerous false positive results. The main objective of this research work is to develop an algorithm for automated image processing and analysis of the extracted features on thermal images for screening or pre-diagnosis of the diseases. This work presents the results of our previously developed Copyright algorithm applied to thermal images obtained from the patients with Axial Spondyloarthritis. This processing and analysis of the extracted features from thermal images can offer a novel quick and non-invasive tool for diagnosis and monitoring of rheumatoid diseases.
KW - Automated segmentation
KW - Image processing
KW - Medical thermal images
KW - Rheumatic diseases
UR - http://www.scopus.com/inward/record.url?scp=85084831797&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45124-0_36
DO - 10.1007/978-3-030-45124-0_36
M3 - Conference contribution
AN - SCOPUS:85084831797
SN - 978-3-030-45123-3
T3 - IFIP Advances in Information and Communication Technology
SP - 378
EP - 386
BT - Technological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Farhadi, Nastaran
A2 - Lopes, Fábio
A2 - Pereira, Helena
PB - Springer
CY - Cham
T2 - 11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
Y2 - 1 July 2020 through 3 July 2020
ER -