Year: 2025
Type: article
Abstract: Raman micro-spectroscopy is a powerful, label-free technique for biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spectroscopic imaging to extract meaningful biochemical information. Traditional methods, such as K-means clustering with Euclidean distance, often struggle to capture subtle spectral variations, leading to suboptimal segmentation. Alternative distance metrics, including cosine and Mahalanobis distances, have been explored to enhance cluster separability, yet challenges remain in distinguishing chemically relevant features while minimizing redundancy and noise. In this study, we introduce an asymmetric metric distance matrix with a tunable eccentricity parameter to improve clustering performance in Raman hyperspectral imaging. Our results demonstrate that suitable eccentricity values enhance the identification of subcellular structures while requiring fewer clusters than Euclidean-based approaches. Compared to polar metrics, the proposed asymmetric metric achieves better stability and reduced noise, leading to more accurate segmentation. Future research could explore its application in other clustering techniques and machine learning frameworks, as well as its application in broader spectral imaging techniques where distance metric plays a fundamental role.
Source: Applied Sciences
DOI: 10.3390/app15084461
Authors: Bernadette Scopacasa, Patrizio Candeloro
Institution: Magna Graecia University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409535834