A Dataset for Evaluating Blood Detection in Hyperspectral Images

TL;DR

This paper introduces a new hyperspectral blood detection dataset to aid development of machine learning algorithms, demonstrating its utility through detection experiments and highlighting challenges in the field.

Contribution

The paper provides the first open-access hyperspectral blood detection dataset with diverse scenarios to support algorithm development and evaluation.

Findings

The dataset enables testing of detection algorithms across various conditions.

Detection experiments reveal challenges in hyperspectral blood detection.

Results serve as a benchmark for future research.

Abstract

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.