Medical image retrieval based on visual contents and text information

A new medical image retrieval approach based on low-level features and high-level semantic features is proposed. The image is segmented into several sub-images by fuzzy C-mean clustering algorithm. After extracting the three gray features, in order to emphasize shape and texture features, the sub-image is changed to binary image, and seven shape features and four texture features are extracted. To decrease feature vector dimension, genetic algorithm is used. The optimizing features constitute the visual contents. Only using these low-level features can't get satisfying results, because these features can't entirely describe human recognition to images, the semantic features are needed, but it is difficult to extract the image semantic contents directly at the present time, so the text information in the image report by radiologists is chosen for semantic content. Experimental results show that the retrieval integrating low-level visual features and text information is better than that only by visual contents.