Literature DB >> 33477091

Chronic wounds multimodal image database.

Michał Kręcichwost1, Joanna Czajkowska2, Agata Wijata2, Jan Juszczyk2, Bartłomiej Pyciński2, Marta Biesok2, Marcin Rudzki2, Jakub Majewski3, Jacek Kostecki4, Ewa Pietka2.   

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.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chronic wounds; Colour photography; Depth map; Manual outlines; Multimodal dataset; Segmentation and registration; Thermal imaging

Year:  2021        PMID: 33477091     DOI: 10.1016/j.compmedimag.2020.101844

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  1 in total

1.  Tomographic reconstruction from planar thermal imaging using convolutional neural network.

Authors:  Daniel Ledwon; Agata Sage; Jan Juszczyk; Marcin Rudzki; Pawel Badura
Journal:  Sci Rep       Date:  2022-02-11       Impact factor: 4.379

  1 in total

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