Research On Feature Extraction For Content-Based Medical Image Retrieval

With the rapid improvement of image-based clinical technique, large amounts of digital images are produced everyday. The management of medical image database and how to use those images in Clinical Diagnose Process (CDP) becomes an especially challenge in medical area. Content-Based Image Retrieval plays an important role in clinic, teaching, research and PACS, etc.Many medical images don't contain color information or are adopted under the limited conditions. Texture and shape features are more important in the medical image retrieval, compared with the features of color or gray. In the context, we focus on the image's texture and shape features extraction.For texture feature, we propose an algorithm of Gray-Primitive Co-occurrence Matrix that combines the space-statistical texture algorithm of Gray Level Co-occurrence Matrix with the structure distribution of surrounding pixels. Experiment results verify this method is robust, relatively feeble sensitivity to rotation and better query performance.For texture feature, making use of the validity of wavelet edge detection and relative moment depiction of the region and structure, we propose a shape feature extraction based on wavelet and relative moments. First, it transforms the luminance images with wavelet edge detection to get multi-scale edge images, then calculates the six relative moments of every scale. Similarity is given by the Wikipedia distance between two images' normalized moment vectors. Experiment results verify this method is application independent and effective to solve the problem brought out by image translation, scaling, rotation. Meanwhile, the paper propose blurry algorithm to improve the defect of thick edge and overabundance details. It eliminates redundancy data, obtains accurate object shape and reduces the query error to the least degree.At last, we develop a simple experimental medical image retrieval system based on example image and certificate the following algorithm.