tailieunhanh - Dynamic semi supervised fuzzy clustering for dental X-ray image segmentation: An analysis on the additional function

Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. | Journal of Computer Science and Cybernetics, , (2015), 323–339 DOI: DYNAMIC SEMI-SUPERVISED FUZZY CLUSTERING FOR DENTAL X-RAY IMAGE SEGMENTATION: AN ANALYSIS ON THE ADDITIONAL FUNCTION TRAN MANH TUAN1 , LE HOANG SON2 , LE BA DUNG3 1 School of Information and Communication Technology, Thai Nguyen University; tmtuan@ 2 VNU University of Science, Vietnam National University; sonlh@ 3 Institute of Information Technology, VAST; lbdung@ Abstract. Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. In our previous work, the authors presented a semi-supervised fuzzy clustering algorithm using interactive fuzzy satisficing named as SSFC-FS for this problem. An important issue of SSFC-FS is that the pre-defined membership matrix is a fixed function in the sense that it uses the same structure and parameters for all dental images. This is a shortcoming of SSFC-FS since each image has its own structure and morphology so that it needs different membership matrices. In this paper, the authors propose another new dynamic semi-supervised fuzzy clustering called SSFC-FSAI that extends SSFC-FS by employing a collection of pre-defined membership matrices for dental images. A procedure to choose a suitable pre-defined membership matrix for a given dental X-ray image is proposed and attached to SSFC-FSAI. Experimental results on a real dataset of 56 dental X-ray images from Hanoi University of Medical in 2014 – 2015 show that SSFC-FSAI has better accuracy than SSFC-FS and the relevant algorithms. Keywords. Additional function, dynamic semi-supervised