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dc.contributor.authorUklid Yeesarapaten_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorChatpat Kongpunen_US
dc.description.abstractDental fluorosis occurs in many parts of the world because of highly exposure to high concentration of fluoride in the teeth development stage. To help the health policy makers developing the prevention and treatment plans, a manual or automatic image-based dental fluorosis classification system is needed. In this paper, we develop an automatic dental fluorosis classification system using multi-prototypes derived from the fuzzy C-means clustering algorithm. The values from red, green, blue, hue, saturation, and intensity channels are utilized as features in the algorithm. We also set the dental fluorosis classification criteria from the amount of pixels belonging to each class. We found that the pixel correct classification rate is around 92% on the training data set and around 90% on the blind test data set when comparing the results with two experts. Three out of seven images in the training data set and eight out of fifteen images in the blind test data set are correctly classified into dental fluorosis classes. © 2014 IEEE.en_US
dc.subjectComputer Scienceen_US
dc.titleDental fluorosis classification using multi-prototypes from fuzzy C-means clusteringen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014en_US Mai Universityen_US Ministry of Public Healthen_US
Appears in Collections:CMUL: Journal Articles

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