| Autor: |
Annadatha, Sowmya, Shen, Ming, Rahbek, Ole, Kold, Søren Vedding, Fridberg, Marie |
| Rok vydání: |
2022 |
| Zdroj: |
Annadatha, S, Shen, M, Rahbek, O, Kold, S V & Fridberg, M 2023, A Tool for Thermal Image Annotation and Automatic Temperature Extraction around Orthopedic Pin Sites . in IEEE Image Processing, Applications and Systems . Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS2022), Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS 2022), 05/12/2022 . |
| Popis: |
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 datasetsfor 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. Acquiring single-click manual annotations by processing thermal images and further, saving these annotations in an acceptable format for training supervised machine learning models with an add on feature to automatically map these annotations onto radiometric images to extract, save temperature distributions is the novelty of the proposed work and is also crucial for research on deep learning-based investigation on surgical wound infections. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
