Paper Info
Title
Graph entropy-based clustering algorithm in medical brain image database.
Abstract
The high incidence of brain tumor has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors' diagnosis. Clustering medical images for Intelligent Decision Support is an important part in the field of medical image mining because there are several technical aspects which make this problem challenging. In this paper, we propose a medical brain image clustering method to find similar pathology images that can assist doctors to analyze the specific disease, discover its potential cause and make more accurate treatment. Firstly, this method represents medical brain image dataset as a weighted, undirected and complete graph. Secondly, this graph is sparsified so as to describe the similarity of medical images very well. Last but not the least, a graph entropy based clustering method for this sparsified graph is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity. The clustering results can better describe the similarity of medical images.
Year
DOI
Venue
2016
10.3233/JIES-169032
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Medical image,graph entropy,sparsification,clustering
Data mining,CURE data clustering algorithm,Artificial intelligence,Time complexity,Cluster analysis,Complete graph,Canopy clustering algorithm,Graph database,Pattern recognition,Correlation clustering,Graph entropy,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
31
SP2
1064-1246
Citations 
PageRank 
References 
0
0.34
17
Authors
5
Name
Order
Citations
PageRank
Yu Zhan100.68
Haiwei Pan25221.31
Xiaoqin Xie31810.36
Zhiqiang Zhang411425.82
Wenbo Li510.77