Teeth segmentation from dental x-ray image by template matching

Poonsri, Arisa; Aimjirakul, Napapa; Charoenpong, Theekapun; Sukjamsri, Chamaiporn

doi:10.1109/bmeicon.2016.7859599

Cited by 17 publications

(14 citation statements)

References 10 publications

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“…Grzegorzek et al [5] presented a multi-stage approach for teeth segmentation from dentition surfaces based on a 2D model-based contour retrieval algorithm. Poonsri et al [6] proposed a method to segment teeth from a panoramic dental x-ray image by means of tooth area identification and template matching. However, these methods are unreliable for segmenting teeth in terms of irregular teeth arrangements and indistinctive tooth boundary, and which cannot accurately express the 3D shape of teeth.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

et al. 2019

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To solve the problem of low efficiency, the complexity of the interactive operation, and the high degree of manual intervention in existing methods, we propose a novel approach based on the sparse voxel octree and 3D convolution neural networks (CNNs) for segmenting and classifying tooth types on the 3D dental models. First, the tooth classification method capitalized on the two-level hierarchical feature learning is proposed to solve the misclassification problem in highly similar tooth categories. Second, we exploit an improved three-level hierarchical segmentation method based on the deep convolution features to conduct segmentation of teeth-gingiva and inter-teeth, respectively, and the conditional random field model is used to refine the boundary of the gingival margin and the inter-teeth fusion region. The experimental results show that the classification accuracy in Level_1 network is 95.96%, the average classification accuracy in Level_2 network is 88.06%, and the accuracy of tooth segmentation is 89.81%. Compared with the existing state-of-the-art methods, the proposed method has higher accuracy and universality, and it has great application potential in the computer-assisted orthodontic treatment diagnosis.

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Section: Introductionmentioning

confidence: 99%

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

et al. 2019

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“…It has been observed that the segmentation of panoramic X-rays using wavelet transformation shows better results than adaptive and iterative thresholding ( Patanachai, Covavisaruch & Sinthanayothin, 2010 ). Another fully automatic segmentation of the teeth using the template matching technique introduced by Poonsri et al (2016) shows 50% matching accuracy results. In Razali et al (2014) analyzed X-rays for the age estimations by comparing edge detection approaches.…”

Section: Methodsmentioning

confidence: 99%

“…The dental X-ray image analysis methods can be categorized in several categories: region growing techniques, edge detection methods, thresholding based, clustering techniques, level set, and active contour, etc., are presented in ‘Image processing methods for dental image analysis’ ( Mahoor & Abdel-Mottaleb, 2004 ; Zhou & Abdel-Mottaleb, 2005 ; Nomir & Abdel-Mottaleb, 2005 , 2007 ; Gao & Chae, 2008 ; Oprea et al, 2008 ; Patanachai, Covavisaruch & Sinthanayothin, 2010 ; Harandi & Pourghassem, 2011 ; Hu et al, 2014 ; Amer & Aqel, 2015 ; Zak et al, 2017 ; Avuçlu & Bacsçiftçi, 2020 ) ( Rad et al, 2015 ; Tuan, Ngan & Son, 2016 ; Poonsri et al, 2016 ; Son & Tuan, 2016 , 2017 ; Ali et al, 2018 ; Alsmadi, 2018 ; Obuchowicz Rafałand Nurzynska et al, 2018 ; Tuan et al, 2018 ; Fariza et al, 2019 ; Kumar, Bhadauria & Singh, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Descriptive analysis of dental X-ray images using various practical methods: A review

Kumar¹,

Bhadauria²,

Singh³

2021

PeerJ Computer Science

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In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

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“…Image segmentation is done to distinguish between foreground in this case is a dental object, with the background in this case is a part other than teeth [2]. The segmentation process of dental panoramic radiograph has been carried out and get good 26 Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information), volume 13, issue 1, February 2020 results with various algorithms when applied to a single tooth image [3]- [5]. But this becomes a problem when faced with overlapping teeth [6].…”

Section: Introductionmentioning

confidence: 99%

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Adam

Arifin

2020

Jurnal Ilmu Komputer dan Informasi

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To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

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Teeth segmentation from dental x-ray image by template matching

Cited by 17 publications

References 10 publications

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

Automatic Classification and Segmentation of Teeth on 3D Dental Model Using Hierarchical Deep Learning Networks

Descriptive analysis of dental X-ray images using various practical methods: A review

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

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