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.

Image processing research group applied immersive experience into “medical image processing”. A scheme named “introduction by game, case driven, lecture on specific theme, grouping experiment, and seminar”was designed and practiced. We designed a game for each subject and made students immersed in the game to feel the pleasure of learning. We changed the traditional teaching method which focused on every point of knowledge, presented and then decomposed the case before teaching each knowledge point which was related to the case. By grouping experiment and seminar, students can learn actively. We also developed a virtual experiment platform with .NET and Matlab for students to develop learning.