Agne Kadusauskiene, University of Health Sciences Kauno Klinikos, Lithuania
Title : Algorithm for Automated Foot Detection in Thermal and Optical Images for Temperature Asymmetry Analysis
Infrared thermography has been proven to be an effective non-invasive method in diabetic foot ulcer prevention, yet current image processing algorithms are inaccurate and impractical for clinical work.
The aim of this study was to investigate the accuracy of the proposed automated algorithm for feet outline detection and localization of potential inflammation regions in thermal images using a tablet and a mobile thermal camera.
In order to achieve pixel-wise accuracy, the edges of both thermal and optical images were merged into an edge image and used for the estimation of foot template transformations during the localization process. According to the feet template transformations, temperature maps were calculated and compared with each other to detect a set of regions exceeding the defined temperature threshold. Finally, a set of simulated inflammation regions were filtered according to the blobs features to obtain the final list of inflammation regions.
The developed algorithm yielded 95.83% accuracy for foot outline detection and 94.28% accuracy for detection of the inflammation regions.
The proposed algorithm is completely automated and does not require the interference of an operator. This system will be implemented using a mobile application where optical and thermal images are acquired, processed, and analyzed for possible ulceration.
Agne Kadusauskiene is a senior residend of Endocrinology at Lithuanian University of Health Sciences Kauno Klinikos. Her academical and research interest focuses on diabetes and hypogonadism. She has done an internship in Endocrinology at Parklan Memorial Hospital, UT Southwestern Medical Center, Dallas, USA. She has been a representative of Endocrinology in The European Junior Doctors Association (EJD) since 2018. Also, she is medical adviser of Diabetis, JSC.