A Dataset for Evaluating Blood Detection in Hyperspectral Images
This work provides a dataset for researchers in medical imaging or forensics to evaluate machine learning methods for blood detection in hyperspectral images, but it is incremental as it builds on existing detection algorithms.
The authors tackled the challenge of developing blood detection algorithms for hyperspectral images by creating a new open-access dataset with multiple scenarios, and they tested it using a Matched Filter detector to highlight detection challenges and provide a reference for future work.
The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published in accordance to open access mandate, consist of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We explored the dataset with blood detection experiments. We used hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.