A Tool for Thermal Image Annotation and Automatic Temperature Extraction around Orthopedic Pin Sites

Abstract

Existing annotation tools are mainly designed for visible images to support supervised learning problems for machine learning. A few tools exist for extracting temperature information from thermal images. However, they are time and manpower consuming, require different stages of data management, and are not automated. This paper focuses on addressing the limitation of existing tools in handling big thermal datasets for annotation, temperature distribution extraction in the Region of Interest (ROI) of Orthopedic surgical wounds and provides flexibility for a researcher to integrate thermal image analysis into wound care machine learning models. We present an easy to use research tool for one click annotation of Orthopedic pin sites for extraction of thermal information, which is a preliminary step of research to estimate the reliability of thermography for home based surveillance of post-operative infection. The proposed tool maps annotations from visible registered image onto thermal and radiometric images. Mapping these annotations from visible registered images avoids manual bias in annotating thermal images. Integrating the functionality of an annotation tool by processing thermal images to acquire single-click manual annotations and extracting temperature distributions in the ROI with those acquired annotations is the novelty of the proposed work and is also crucial for research on deep learning-based investigation on surgical wound infections.