Assessing Processing Strategies on Data from Medical Hyperspectral Acquisition Systems

Abstract

Hyperspectral imaging (HSI) has gained prominence in medical diagnostics due to its ability to capture and analyse detailed spectral information beyond human visual capabilities. Processing of HSI data is essential to enhance subsequent analysis and ensure the accuracy of results by reducing noise and unwanted artifacts. This paper provides an overview of state-of-the-art processing methods for HSI data, focusing on smoothing, normalization, and spectral derivatives. The efficacy of these methods is evaluated using root mean square error (RMSE) to compare pre-processed data with wavelength reference standard, alongside execution time considerations. Results indicate that certain algorithms, such as smoothing based on moving average, standard normal variate, and first spectral derivatives, yield superior performance across different medical HSI systems. Additionally, combining these processing techniques further improves data fidelity to the wavelength reference standard. Overall, this study offers insights into optimal processing strategies for enhancing the accuracy and reliability of HSI data.