Kernel generalized-gaussian mixture model for robust abnormality detection

Abstract

Typical methods for abnormality detection in medical images rely on principal component analysis (PCA), kernel PCA (KPCA), or their robust invariants. However, typical robust-KPCA methods use heuristics for model fitting and perform outlier detection ignoring the variances of the data within principal subspaces. In this paper, we propose a novel method for robust statistical learning by extending the multivariate generalized-Gaussian distribution to a reproducing kernel Hilbert space and employing it within a mixture model. We propose expectation maximization to fit our kernel generalized-Gaussian mixture model (KGGMM), using solely the Gram matrix and without the explicit lifting map. We exploit the KGGMM, including component means, principal directions, and variances, for abnormality detection in images. The results on 4 large publicly available datasets, involving retinopathy and cancer, show that our method outperforms the state of the art.