关键词: 多模态, 特征抽取, 图像聚类, 医学图像

Abstract: Low-level semantic features (such as color and texture, etc.) are widely used to compare the similarity of images for the existing image feature representation. However doctors usually judge the similarity of medical images based on the differences between high-level semantic features (such as lesions and lesion types, etc.) in the local region. This paper proposes a novel multimodal features medical image clustering method which combines the medical image texture features and morphological features. This method can solve the problem that traditional methods ignore the high-level semantic features of medical images, which leads to the poor clustering effect of medical images. Firstly, a texture feature fusion method is proposed to represent the global underlying semantic features of medical images, on the other hand, the morphological description of the region of interest (ROI) is proposed as the local high-level semantics of medical images feature. Secondly, the similarity measure is used to calculate the similarity between the two types of features for CT images. Finally, the learning of the feature fusion weight is studied by using the multi-kernel learning method, and the validity of the method is verified by using the multi-kernel spectral clustering experiment.

Key words: multimodal, feature extraction, image clustering, medical images