Paper Info
Title
Brain CT image similarity retrieval method based on uncertain location graph.
Abstract
A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.
Year
DOI
Venue
2014
10.1109/JBHI.2013.2274798
IEEE J. Biomedical and Health Informatics
Keywords
Field
DocType
uncertain graph,image modeling,computerised tomography,uncertain location graph,computed tomography,image similarity retrieval,computer-aided diagnosis systems,image retrieval,ulg,brain ct image similarity retrieval method,brain,medical image,graph theory,image texture,effective index structure,medical image processing,uncertainty,indexes,biomedical imaging
Graph theory,Graph,Computer vision,Pattern recognition,Medical imaging,Image texture,Computer science,Image retrieval,Computed tomography,Artificial intelligence,Image database,Image registration
Journal
Volume
Issue
ISSN
18
2
2168-2208
Citations 
PageRank 
References 
6
0.47
16
Authors
6
Name
Order
Citations
PageRank
Haiwei Pan15221.31
Pengyuan Li2165.81
Qing Li33222433.87
Qilong Han415619.26
Xiaoning Feng5194.78
Linlin Gao683.87