The classical deformable models and some new approaches for the past few years are surveyed,especially de-scribe two important methods of them: snake model and level-set model.Image segmentation is the bases of 3D recon-struction technology and medical visualization image,which are meaningful to disease diagnosis and therapy in clinical.It is not only a key step and critical technology in medical image processing and image analysis but also a classic puzzle.With the need of application,it is very important to continually research the image segmentation,to increasingly improve the old approaches and introduce the new and more effective ones.

Hyperlipidemia is one of the main basic lesions of blood vessels and organ diseases. Traditional Chinese medicine (TCM) has been used in the prevention and treatment of hyperlipidemia with advantages such as little drug side effects and obvious efficacy. The establishment of hyperlipidemia diagnostic aid platform helped to improve treatment efficiency, and provide a platform for data acquisition, storage and analysis for research on hyperlipidemia syndrome standardization. At first, a collection system was implemented, which was consisted of tongue image and the text information of the other three diagnostic methods, for doctor's accurate mastering of patient's clinical manifesta-tions and improvement of therapeutic effect. Through this platform, a clustering algorithm was applied to analyze clin-ical information of four diagnostic methods among 897 hyperlipemia cases, which had been classified into 5 cate-gories. Compare disease characteristics of samples in every category with syndromes category and find out the rela-tionships between them, by which the foundation of hyperlipemia syndrome differentiation standardization will be es-tablish. It provided suggestions on TCM syndrome differentiation and diagnosis.

BACKGROUND: Content-based medical image retrieval involves multiple domains.Due to different imaging principles of various medical images,there are differences in color,texture,and shape,which should be resolved.OBJECTIVE: As in content-based medical image retrieval system,feature extraction from image is very difficult and the retrieval is very time-consuming,a similar image retrieval method based on graph-cuts and rough sets is proposed.METHODS: In order to overcome the defects that graph-cuts is only suitable for small image end easily leads to a small cut-sets,a clustering was applied to image,and the Gomory-Hu cuts tree of image was established.An image feature library was built by removing the edges of Gomory-Hu cuts tree for the value of cut.Reduction of features in library was obtained based on rough sets and the number of features in similar compare decrease.This method was applied to retrieve brain tumor image in MRI brain image database.RESULTS AND CONCLUSION: Results show that this method can effectively retrieve brain tumor images in the library.The average retrieval precision rate and the average recall rates were 78.4% and 62.9%,respectively.