Automatic body part and pose detection in medical infrared thermal images

ABSTRACT

Automatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician’s determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu’sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.

KEYWORDS:

• Medical infrared thermal imaging
• pose detection
• computer-aided medical diagnosis

Acknowledgments

This study was approved by the Erciyes University Ethical Council of Clinical Studies, Kayseri, Turkey (2019/524) and was carried out in collaboration with Dr. M.M. YILMAZ Clinic in Kayseri, Turkey, and we thank Dr. Mustafa Mücahit Yılmaz for his sincere help. We would like to thank Assoc. Prof. Zafer Aydın from Abdullah Gul University for his constructive and competent criticism on the scientific content of this work.

The dataset used in this study will be provided upon request.

Disclosure statement

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Ahmet Özdil

Ahmet Özdil obtained his bachelor’s degree from Yıldız Technical University, İstanbul, Turkey in 2008 and master’s degree from Melikşah University, Kayseri, Turkey in 2015. He is currently pursuing a PhD in Electrical and Computer Engineering at Abdullah Gül University, Kayseri, Turkey specializing in medical infrared thermal image processing and classification. He has got 10 years of research experience in the field of Computer Science. His research interests include medical image processing, computer vision and biometric image processing & recognition, signal processing.

Bülent Yılmaz

Bülent Yılmaz is a Professor of Electrical-Electronics Engineering at Abdullah Gül University (AGU), Kayseri, Turkey. He has got over 20 years of research experience in the fields of Biomedical Image and Signal Processing and Machine Learning. Prof. Yilmaz received his BS and MS degrees from Electrical-Electronics Engineering at the Middle East Technical University (METU), Ankara, Turkey in 1997 and 1999 respectively. He got his PhD degree from Bioengineering Department of the University of Utah, Salt Lake City, Utah, USA in 2004. His current research interests are biomedical signal and image processing, especially brain-computer interfaces and automatic disease detection. He is the principal investigator at the Biomedical Instrumentation and Signal Analysis (BISA) Laboratory at AGU. He is currently serving as the vice rector of AGU.