Content-Based Medical Image Retrieval

With the development of multimodality medical imaging equipments, the medical images have become indispensable to modern clinical diagnosis and medical research. A great number of images are generated in a hospital everyday. It becomes an urgent problem to be solved how to organize, manage and index medical images in so large scale. The traditional information retrieval techniques, which are based on text comparison, are not satisfied to retrieve large scale medical image databases. New image retrieval methods should be explored. It is a very promising idea to introduce content-based image retrieval (CBIR) technique into indexing medical image databases, so we studied the algorithms and techniques of content-based medical image retrieval in the thesis.In this paper, we first propose a content-based medical image retrieval approach based on anatomical structure under fixed rectangular segmentation mode. Some features are then used to describe the information of each block, such as histogram and its statistical features, and texture, which are applied in our retrieval method. Because of drawbacks of fixed segmentation, a new region-based retrieval method based on gray cluster and Hopfield Neural Networks is applied in this paper. Then the features of main blocks of auto-segmented image are extracted and used for in image retrieval.Finally, in order to improve the efficiency and accelerate the retrieval process, we give some insight into relevant image database index mechanism. Especially, we discuss thoroughly about M-Tree index mechanism, which we applied in our CBIR system.