Name
Abstract | ||
|---|---|---|
A multimodal wound image database was created to allow fast development of computer-aided approaches for wound healing monitoring. The developed system with parallel camera optical axes enables multimodal images: photo, thermal, stereo, and depth map of the wound area to be acquired. As a result of using this system a multimodal database of chronic wound images is introduced. It contains 188 image sets of photographs, thermal images, and 3D meshes of the surfaces of chronic wounds acquired during 79 patient visits. Manual wound outlines delineated by an expert are also included in the dataset. All images of each case are additionally coregistered, and both numerical registration parameters and the transformed images are covered in the database. The presented database is publicly available for the research community at https://chronicwounddatabase.eu. That is the first publicly available database for evaluation and comparison of new image-based algorithms in the wound healing monitoring process with coregistered photographs, thermal maps, and 3D models of the wound area. Easily available database of coregistered multimodal data with the raw data set allows faster development of algorithms devoted to wound healing analysis and monitoring. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compmedimag.2020.101844 | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS |
Keywords | DocType | Volume |
Chronic wounds, Multimodal dataset, Manual outlines, Segmentation and registration, Colour photography, Thermal imaging, Depth map | Journal | 88 |
ISSN | Citations | PageRank |
0895-6111 | 1 | 0.63 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michał Kręcichwost | 1 | 1 | 0.63 |
| Joanna Czajkowska | 2 | 1 | 0.63 |
| Agata Wijata | 3 | 1 | 0.96 |
| Jan Juszczyk | 4 | 1 | 0.63 |
| Bartłomiej Pyciński | 5 | 1 | 0.63 |
| Marta Biesok | 6 | 1 | 1.98 |
| Marcin Rudzki | 7 | 1 | 0.63 |
| Jakub Majewski | 8 | 1 | 0.63 |
| Jacek Kostecki | 9 | 1 | 0.63 |
| Ewa Pietka | 10 | 1 | 0.63 |