Medical Hyperspectral Image Feature Selection Framework Using Functional Data Analysis: Application to Membranous Nephropathy Pathologic Diagnosis

To address the core issue of high-dimensional data processing in hyperspectral pathological diagnosis, we develop a new feature selection framework using functional data analysis (FSFDA). The framework models pixel spectra as continuous functions to preserve spectral continuity, overcoming the limitations of traditional discrete representations. Based on functional features, an innovative adaptive spectral segmentation strategy driven by functional change rate is developed to achieve optimal segmentation in the feature space. Additionally, a multi-criteria scoring mechanism including supervised (FSFDA-S) and unsupervised (FSFDA-U) paradigms is developed to enhance feature diagnostic discriminability while maintaining sparsity. Experimental results on the pathological hyperspectral image dataset of membranous nephropathy validate that the proposed method achieves over 99% classification accuracy while reducing feature dimensions by 94.5%. For cross-modal data involving in-vivo human brain and white blood cells, FSFDA effectively identifies diagnostic bands aligned with histopathological signatures, verifying its adaptive feature selection ability and cross sample generalization performance.