Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering

Dental 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...

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Main Authors: Uklid Yeesarapat, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Chatpat Kongpun
Format: Conference Proceeding
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904430955&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45484
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-454842018-01-24T06:11:07Z Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering Uklid Yeesarapat Sansanee Auephanwiriyakul Nipon Theera-Umpon Chatpat Kongpun Dental 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. 2018-01-24T06:11:07Z 2018-01-24T06:11:07Z 2014-01-01 Conference Proceeding 2-s2.0-84904430955 10.1109/CIBCB.2014.6845534 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904430955&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45484
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description Dental 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.
format Conference Proceeding
author Uklid Yeesarapat
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Chatpat Kongpun
spellingShingle Uklid Yeesarapat
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Chatpat Kongpun
Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
author_facet Uklid Yeesarapat
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Chatpat Kongpun
author_sort Uklid Yeesarapat
title Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
title_short Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
title_full Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
title_fullStr Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
title_full_unstemmed Dental fluorosis classification using multi-prototypes from fuzzy C-means clustering
title_sort dental fluorosis classification using multi-prototypes from fuzzy c-means clustering
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904430955&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45484
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