A Content-based Retrieval System for Chest CT Image Database

• Main Author: 江志宗
• Other Authors: 余松年
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/27750034372116323658

碩士 === 國立中正大學 === 電機工程研究所 === 90 === The objective of this thesis is to build an image retrieval system for chest CT image databases. Based on the structure of the content-based image retrieval method, we propose a medical image segmentation method which combines physiological knowledge and watershed segmentation algorithm. The purpose of segmentation is to segment mediastinum and lung lobes in a chest CT image. The ARGs(attributed relational graphs) are chosen to describe the features of objects which are segmented previously. Then, image database is constructed by the feature vector of each image. In database searching, two searching modes are provided that are “query by example” and “query by example”. Our system uses Euclidean distance to measure the similarity between the image in query and the image in database. The system output the 30 most similar images in the chest CT image database as query results. The average precision of our system is about 80% which is pretty good in an automatic medical image retrieval system. In accordance with the content-based retrieval system for chest CT image database, searching the meaningful objects in a chest CT image can assist in the diagnosis of chest diseases. The physicians can also utilize the searching result to trace back into the computerized patient record to retrieve useful information.