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Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion.

ABSTRACT: This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage. The metadata related to every image contains the WGS84 coordinates for allocating the images. Also, the homography matrices between every image and the orthoimage are included in the dataset. The images and homographies are compatible with the well-known assessment protocol for detection and description proposed by Mikolajczyk and Schmid [1].

SUBMITTER: Garcia-Moreno LM

PROVIDER: S-EPMC7063128 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion.

García-Moreno Lina M LM Díaz-Paz Jean P JP Loaiza-Correa Humberto H Restrepo-Girón Andrés D AD

Data in brief 20200226

This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenc ...[more]

PMID: 32181295

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Project description:PurposeDeformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets.Acquisition and validation methodsThirty CT image pairs were acquired from several publicly available repositories as well as authors' institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4 mm ± 0.3 mm.Data format and usage notesThe data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung-DIR-QA.Potential applicationsThe dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung.

Dataset Information

Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion.

Publications

Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion.

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