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General Information
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2016 Vol.8(6): 422-427 ISSN: 1793-8236
DOI: 10.7763/IJET.2016.V8.926

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

Tran Manh Tuan and Le Hoang Son
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.

Index Terms—Dental X-ray images, graph-based clustering, medical diagnosis, affinity propagation clustering.

Tran Manh Tuan is with School of Information and Communication Technology, Thai Nguyen University, Vietnam (e-mail: tmtuan@ictu.edu.vn).
Le Hoang Son is with VNU University of Science, Vietnam (e-mail: sonlh@vnu.edu.vn).


Cite: Tran Manh Tuan and Le Hoang Son, "A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis," International Journal of Engineering and Technology vol. 8, no. 6, pp. 422-427, 2016.

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