Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements va...

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Main Authors: Sheng-hui Liao, Shi-jian Liu, Bei-ji Zou, Xi Ding, Ye Liang, Jun-hui Huang
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2015/187173
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spelling doaj-0fea90e2399b49b79e1d277bc3b3e4c32020-11-24T23:26:31ZengHindawi LimitedBioMed Research International2314-61332314-61412015-01-01201510.1155/2015/187173187173Automatic Tooth Segmentation of Dental Mesh Based on Harmonic FieldsSheng-hui Liao0Shi-jian Liu1Bei-ji Zou2Xi Ding3Ye Liang4Jun-hui Huang5School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaDepartment of Stomatology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, ChinaDepartment of Stomatology, Xiangya Hospital of Central South University, Changsha 410008, ChinaXiangya Stomatological Hospital of Central South University, Changsha 410008, ChinaAn important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.http://dx.doi.org/10.1155/2015/187173
collection DOAJ
language English
format Article
sources DOAJ
author Sheng-hui Liao
Shi-jian Liu
Bei-ji Zou
Xi Ding
Ye Liang
Jun-hui Huang
spellingShingle Sheng-hui Liao
Shi-jian Liu
Bei-ji Zou
Xi Ding
Ye Liang
Jun-hui Huang
Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
BioMed Research International
author_facet Sheng-hui Liao
Shi-jian Liu
Bei-ji Zou
Xi Ding
Ye Liang
Jun-hui Huang
author_sort Sheng-hui Liao
title Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
title_short Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
title_full Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
title_fullStr Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
title_full_unstemmed Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields
title_sort automatic tooth segmentation of dental mesh based on harmonic fields
publisher Hindawi Limited
series BioMed Research International
issn 2314-6133
2314-6141
publishDate 2015-01-01
description An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.
url http://dx.doi.org/10.1155/2015/187173
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