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Microscopic hyperspectral imaging has become an emerging technique for various medical applications. However, high dimensionality of hyperspectral image (HSI) makes image processing and extraction of important diagnostic information challenging. In this paper, a novel dimensionality reduction method named spatial-spectral density peaks based discriminant projection (SSDP) is proposed by considering spatial-spectral density distribution characteristics of immune complexes. The proposed SSDP coupled with support vector machine classifier (SVM) yields high-precision automatic diagnosis of membranous nephropathy (MN). Detailed ex-vivo validation of the proposed method demonstrates the potential clinical value of the system in identifying hepatitis B virus-associated membranous nephropathy (HBV-MN) and primary membranous nephropathy (PMN).

Spatial-Spectral Density Peaks Based Discriminant Projection for Classification of Membranous Nephropathy Hyperspectral Pathological Image1