Multi-modal Information Retrieval for Content-based Medical Image and Video Data Mining

Image based medical diagnosis plays an important role in improving the quality of health-care industry. Content based image retrieval (CBIR) has been successfully implemented in medical fields to help physicians in training and surgery. Many radiological and pathological images and videos are generated by hospitals, universities and medical centers with sophisticated image acquisition devices. Images and Videos that help senior or junior physician to practice medical surgery become more and more popular and easier to access through different ways. To help learn the process of a surgery or even make decisions is one of the main objectives of the content based image and video retrieval system. In this paper, a contented-based multimodal medical video retrieval system (CBMVR) for medical image and video databases is addressed. Some key issues are discussed. A new feature representation method named Artificial Potential Field (APF) is addressed which is specially useful in symmetrical imaging feature extraction. Experimental results show that, with this CBMVR, both the senior and junior physicians can benefit from the mass data of medical images and videos.