A Lie group kernel learning method for medical image classification

Abstract

Medical image classification is a basic step in medical image analysis and has been an essential task in computer-aided diagnosis. Existing classification methods are proved to be effective in conventional image classification tasks, but they often achieve a suboptimal performance when applied to medical images characterizing by complex nonlinear variation. Aiming at this challenge, this paper proposes a Lie group kernel learning method for medical image classification by combining Lie group theory, kernel functions, SVM and KNN classifiers. The method represents each image with a Lie group feature descriptor constructed from low-level features and builds a SVM classifier from the training images. Geodesic distances between categorical pivots and each testing image are calculated with Lie group kernel functions to select either the SVM or a KNN classifier to do the classification. The proposed method is applied to three medical image datasets and the results demonstrate the efficacy of the method.