Corner detection and matching methods for brain medical image classification

Abstract

many methods have been developed for corner detection and matching. However, these detection methods do not take the domain knowledge of brain medical images into account. They produce some useless corners and lose essential domain information. Moreover, existing corner matching methods do not consider the uncertainty and structure of brain medical images. And most of them are developed for 3D medical image registration, which are not applicable for 2D image classification. To address these problems, a corner detection method is firstly proposed based on hierarchical textures. Then, based on the uncertainty and structure of brain medical images, a corner matching method is developed to yield an initial and furthermore a maximum matched corner pair sequences. Finally, a similarity function is presented for classification. Experimental results show the proposed corner detection method outperforms the existing method and the classification results based on the maximum matched corner pair sequence are better than the state-of-the-art brain medical image classification methods.