Paper Info
Title
Finding Frequent Approximate Subgraphs in medical image database
Abstract
Medical images are one of the most important tools in doctors' diagnostic decision-making. It has been a research hotspot in medical big data that how to effectively represent medical images and find essential patterns hidden in them to assist doctors to achieve a better diagnosis. Several graph models have been developed to represent medical images. However, the unique structures of domain-specific images are not considered well to lose some essential information. Thus, aiming at brain CT images, we first construct a graph about the Topological Relations between Ventricles and Lesions (TRVL) and present the graph modeling process. Then we propose a method named Frequent Approximate Subgraph Mining based on Graph Edit Distance (FASMGED). This method uses an error-tolerant graph matching strategy that is accordant with ubiquitous noise in practice. Experimental results show that the graph modeling process is computationally scalable and FASMGED can find more significant patterns than current algorithms.
Year
DOI
Venue
2015
10.1109/BIBM.2015.7359821
IEEE International Conference on Bioinformatics and Biomedicine
Keywords
Field
DocType
brain CT images, graph model, graph edit distance, frequent approximate subgraphs
Data mining,Computer science,Theoretical computer science,Artificial intelligence,Image database,Graph database,Matching (graph theory),Big data,Hotspot (Wi-Fi),Machine learning,Graph (abstract data type),Scalability,Graph edit distance
Conference
ISSN
Citations 
PageRank 
2156-1125
2
0.36
References 
Authors
11
7
Name
Order
Citations
PageRank
Linlin Gao183.87
Haiwei Pan25221.31
Qilong Han315619.26
Xiaoqin Xie41810.36
Zhiqiang Zhang511425.82
Xiao Zhai621.37
Pengyuan Li7165.81