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

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).

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.