Fast filtering techniques in medical image classification and retrieval
Xin Yi Zhou, Miaofei Han, Yanli Song, Qiang Li · 2013
Abstract. This article presents the participation of the MIILab (Medi-cal Image Information Laboratory) group in ImageCLEFmed2013. There are three types of tasks for ImageCLEFmed2013: modality classification, image retrieval and compound–image separation. Image modality classi-fication and medical image retrieval are targeted according to MIILab’s research interest. The main goal is to perform a feasibility test on ap-plying existing techniques on new applications, such as applying image denoising techniques on image retrieval and classification. Both global features and local features were employed. Fast filtering tech-niques were used to obtain global features on color, shape and texture. These global features serves to perform a pre–classification on images. Both low–level and high–level local features were extracted. Bags of fea-tures model was used to build final feature vector. Both kNN and SVM classifiers were tried out in modality classification task. Reciprocal kNN was used to perform result fusion in image retrieval task.