The Research On Technics Of Data Mining Applied To The Medical Image

Medical image diagnosis is one of the major methods to make non-injuring diagnosis in medicine. There are many data feature and rule information that will be discovered and recognized in medical image. Now clustering and association rule mining has been a flourishing research area and widely recognized as a key tool for mining valuable information and knowledge from the databases. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. The research on medical image mining has just started. It is significant to research and find powerful data mining algorithms that can handle medical image datasets effectively and efficiently. This situation shapes up the background of researches in this thesis.The gray and density of pixel is a main content showing medical image. The semantic relationship between the gray density of medical image and the anatomy of human organ tissues is discuss here. A feature expression and data processing method, which can be adapted to medical image data mining, is presented. The process of the medical image data mining is offered.Detailed study is done to improve the accuracy of the binned kernel density estimation methods and approximation kernel density function based on grid-based. Clustering and feature extraction algorithms are purposed based on grid-based density function characterization of the medical image dataset. Besides, finding arbitrary shaped clusters also has been proposed successfully. Based on the approximated density construction, the experimental studies for extracting medical image feature have been purposed in the thesis. The processing strategies and experimental results prove the rationality and availability of the novel method.Association rule mining has been applied to large image data. FP-growth algorithm is proposed for enhancing efficiency of Apriori algorithm. The rule that classifies between normal and anomalistic organ image is discovered using association rule mining. DMFACIA (Discovery Maximum Frequent Association Classifier Itemsets Algorithm) is presented and applied to medical image diagnosis in this thesis.