[Medical image retrieval by high level semantic features and low level content features of image].

Content-based image retrieval aims at searching the similar images using low level features,and medical image retrieval needs it for the retrieval of similar images. Medical images contain not only a lot of content data, but also a lot of semantic information. This paper presents an approach by combining digital imaging and communications in medicine (DICOM) features and low level features to perform retrieval on medical image databases. At the first step, the semantic information is extracted from DICOM header for the pre-filtering of the images, and then dual-tree complex wavelet transfrom(DT-CWT) features of pre-filtered images and example images are extracted to retrieve similar images. Experimental results show that by combining the high level semantics (DICOM features) and low level content features (texture) the retrieval time is reduced and the performance of medical image retrieval is increased.

[Dynamic VRML for training in medicine].

Experimenting in medicine can be accomplished by virtual reality system. VRML is a tool to build virtual object and scenes which can realize static and animated applications in medicine. However, to creat a real environment, the demanded level of interactivity and dynamics is difficult to achieve. In this paper we describe some approaches and techniques which can realize dynamic 3D. Our demonstration is based on the implementation of a virtual baby model, whose character can be accomplished by external JAVA applications.