A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis

Abstract

Dental X-Ray image search is an important process in medical diagnosis for diagnosing exactly dental diseases of a patient. This problem is regarded as the matching of a dental X-Ray image with diseases patterns in the database. In this paper, we propose a novel framework using graph-based clustering for dental X-Ray image search. This framework firstly extracts dental features from an X-Ray image to a dental feature database and then uses a vector quantization algorithm to clarify the principal records from the database. Each record is now regarded as a node in a graph which is classified by a graph-based clustering algorithm according to the disease patterns. The dental X-Ray image is classified having disease or non-disease according to other disease patterns in the same group. The new method is experimental validated on a real dataset of 13 dental X-ray images taken from Hanoi Medical University, Vietnam at the period of 2014-2015. Three variants of the framework namely Prim spanning tree (GCP), Kruskal spanning tree (GCK), and Affinity Propagation Clustering (APC) has been implemented. The experimental results suggest the best variant in term of accuracy.