An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments

Li, Shuo; Fevens, Thomas; Krzyżak, Adam; Li, Shangyuan

doi:10.1016/j.compmedimag.2005.10.007

Cited by 47 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the accuracy of segmentation is quite important to successful decisions of diseases for a patient. This problem has been studied extensively in [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] showing that the typical and popular methods are the Otsu thresholding method [11], Fuzzy C-Means (FCM) clustering [1], and Semi-Supervised Entropy regularized Fuzzy Clustering (eSFCM) algorithm [14]. Nevertheless, those methods faced the problems of threshold value determination, determining common boundaries of clusters, and lacking of spatial structures of an X-ray image.…”

Section: Introductionmentioning

confidence: 99%

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

Tuấn

Sơn

Dung³

2016

JCC

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Tuấn

Sơn

Dung³

2016

JCC

View full text Add to dashboard Cite

show abstract

“…A new dynamic energy term was introduced that permitted the directional snake to discriminate the boundary between adjacent teeth. In [13], a variational level set segmentation technique for computer aided dental X-rays analysis was proposed. In [14], variational level set was utilized to detect areas of lesions from periapical dental X-rays.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

et al. 2008

View full text Add to dashboard Cite

Purpose Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. Methods In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed by estimating the arc of the upper and lower jaws and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of M. Hosntalab (B) · A. Abbaspour Tehrani-Fard teeth are performed by employing the horizontal and vertical projections of the panoramic dataset, respectively. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a Variational level set to refine initial teeth boundaries to final contours. Results The proposed algorithm was evaluated in the presence of 30 multi-slice CT datasets including 3,600 images. Experimental results reveal the effectiveness of the proposed method. Conclusion In the proposed algorithm, the variational level set technique was utilized to trace the contour of the teeth. In view of the fact that, this technique is based on the characteristic of the overall region of the teeth image, it is possible to extract a very smooth and accurate tooth contour using this technique. In the presence of the available datasets, the proposed technique was successful in teeth segmentation compared to previous techniques.

show abstract

“…While S. Li et. al [12], have suggested a variation level set segmentation approach for computer aided dental…”

Section: Introductionmentioning

confidence: 99%

Teeth segmentation using dental CT data

Gui

et al. 2014

2014 7th International Conference on Biomedical Engineering and Informatics

View full text Add to dashboard Cite

Cone-beam dental computerized tomography has been introduced as a system geared towards maxillofacial imaging, which is gradually becoming a common tool within the dental industry. Segmentation of teeth from dental CT data is vital for a variety of computer-aided procedures such as dental implants, orthodontic planning and cosmetic surgeries. Teeth segmentations near soft tissue structures can help clinicians to assess the quality of a patient's teeth more easily. Moreover, the ability to identify particular regions surrounding teeth can help to provide better image results generated using 3D surface/volume rendering techniques. This paper, presents an experimental study for teeth segmentation employing two different thresholding techniques. The performances of these techniques are compared with each other. The experimental results show that there is a noticeable difference in segmentation quality between the two methods presented in this paper.

show abstract

An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments

Cited by 47 publications

References 29 publications

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

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

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

Teeth segmentation using dental CT data

Contact Info

Product

Resources

About