Finding Frequent Approximate Subgraphs in medical image database

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.