Research Of Mining Measures To Anxiety Disorders's SPECT Data

To understand the brain and its function is one of the great challenges in 21 century. Now human brain project aims to research the brain function and develops all kinds of tools to analyze and compose the brain and etc. There are more and more mental problems of young people which are paid attention to by the whole world. Number of anxiety disorder and despondent patients increase rapidly. This paper researchs the brain data of despondent patients including normal state and provocative state, in order to find out the high level function and secret of brain.This paper is involved with general measures of SPECT's brain data. In the research of brain image, it is important to process, comform, compose, model, simulate, analyze except for accurately designing experiments, picking up testees and collecting data. Now it mostly focuses on the relations between brain areas and brain functions and simply make sure which part of brain can be functional. The method is to compare the signal of control and mission.This paper uses support vector machines and association rules to min anxiety disorder's SPECT data. It builds a classifier to distinguish testees who are anxiety disorders or not. There are two different ways to extract the brain image features, one uses image semantic classification with texture, shape and color. The other uses region of interest and the max value of t. Then it uses SVMs to classify the SPECT data. After representing brain with a tree-like structure, it uses association rules to min the relations between different brain areas.Finally this paper talks about a knowledge-based brain imaging diagnosing model. According to the properties of brain image, a knowledge-based tree-like representation is introduced to present brain imaging diagnosing model. And the knowledge that is formed by the rules is used with the database and feature extraction to realize the diagnosing model.