Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion.

Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the interpretability of spectral information for biological tissue analysis. The pipeline supports modular hyperspectral data processing, fusion of complementary wavelength ranges, and scalable data storage, and was implemented in Python 3.13.3. The pipeline was evaluated using hyperspectral imaging data acquired from a coronal mouse brain section. Clustering-based analysis and spectral correlation metrics were applied to assess the impact of multimodal data fusion on spectral representation. Clustering of individual modalities yielded silhouette coefficients of 0.5879 for near-infrared data, 0.6020 for mid-infrared data, and 0.6715 for RGB data. Multimodal fusion reduced the silhouette coefficient to 0.5420 and enabled the identification of anatomical structures that were not distinguishable in any single modality. High spectral correlation coefficients exceeding 0.98 confirmed that spectral fidelity was preserved during fusion. These results demonstrate that automated multimodal hyperspectral data fusion can enhance the interpretability of biological tissue despite reduced clustering compactness. The proposed pipeline provides a structured framework for preclinical hyperspectral imaging workflows and supports exploratory biological analysis in medical imaging contexts.