Automatic Segmentation of Mandibular Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting

Indraswari, Rarasmaya; Arifin, Agus Zainal; Suciati, Nanik; Astuti, Eha Renwi; Kurita, Takio

doi:10.22266/ijies2019.0831.13

Cited by 11 publications

(12 citation statements)

References 28 publications

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“…Automatic methods for bone segmentation are very important in order to get 3D surfaces. It helps surgeons in making correct diagnoses, in performing accurate and quicker VSP and in the evaluation of postoperative follow-up without the influence of surgeon's experience [23][24][25][26][27]. The aim of this study was to evaluate method's accuracy for bone segmentation in CBCT data sets.…”

Section: Discussionmentioning

confidence: 99%

Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set

Vaitiekūnas

Jegelevičius

Sakalauskas³

et al. 2019

Applied Sciences

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Due to technical aspects of Cone Beam Computed Tomography (CBCT), the automatic methods for bone segmentation are not widely used in the clinical practice of endodontics, orthodontics, oral and maxillofacial surgery. The aim of this study was to evaluate method’s accuracy for bone segmentation in CBCT data sets. The sliding three dimensional (3D) window, histogram filter and Otsu’s method were used to implement the automatic segmentation. The results of automatic segmentation were compared with the results of segmentation performed by an experienced oral and maxillofacial surgeon. Twenty patients and their forty CBCT data sets were used in this study (20 preoperative and 20 postoperative). Intraclass Correlation Coefficients (ICC) were calculated to prove the reliability of surgeon segmentations. ICC was 0.958 with 95% confidence interval [0.896 ... 0.983] in preoperative data sets and 0.931 with 95% confidence interval [0.836 ... 0.972] in postoperative data sets. Three basic metrics were used in order to evaluate the accuracy of the automatic method—Dice Similarity Coefficient (DSC), Root Mean Square (RMS), Average Distance Error (ADE) of surfaces mismatch and additional metric in order to evaluate computation time of segmentation was used. The mean value of preoperative DSC was 0.921, postoperative—0.911, the mean value of preoperative RMS was 0.559 mm, postoperative—0.647 mm, the ADE value of preoperative cases was 0.043 mm, postoperative—0.057 mm, the mean computational time to perform the segmentation was 46 s. The automatic method showed clinically acceptable accuracy results and thus can be used as a new tool for automatic bone segmentation in CBCT data. It can be applied in oral and maxillofacial surgery for performance of 3D Virtual Surgical Plan (VSP) or for postoperative follow-up.

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Section: Discussionmentioning

confidence: 99%

Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set

Vaitiekūnas

Jegelevičius

Sakalauskas³

et al. 2019

Applied Sciences

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“…In this experiment, we compared modified Otsu's thresholding as the automatic marking method with other automatic segmentation methods, which are Gaussian Mixture Model (GMM) [24] and Histogram Cluster Analysis (HCA) [25]. GMM and HCA methods are selected to provide equal comparison because the process of automatic marking uses automatic segmentation method that separates the grayscale intensity into four regions.…”

Section: Comparison Of Automatic Marking Methodsmentioning

confidence: 99%

“…GMM models the histogram of grayscale intensity in the image as a mixture of several Gaussian distributions. The mean and standard deviation of each Gaussian distribution will be updated to maximize the likelihood of the graylevels assigned in each Gaussian distribution [24]. The result of GMM is k Gaussian distributions that represent the distribution of grayscale intensity in the histogram, with( − 1) thresholds that separate each of the Gaussian distribution.…”

Section: Comparison Of Automatic Marking Methodsmentioning

confidence: 99%

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

Self Cite

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Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

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“…Therefore, the thresholding method greatly influences the final determination of the initial point. In this study we compare the method we propose with 2 comparative thresholding methods, namely the Gaussian Mixture Model (GMM) [21] and Modified Otsu's thresholding [6]. GMM method was chosen as a comparison because of its ability to do multi-thresholding the same as the HCA method.…”

Section: Evaluation Of Automatic Initializationmentioning

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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Automatic Segmentation of Mandibular Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting

Cited by 11 publications

References 28 publications

Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set

Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set

Detection of Overlapping Teeth on Dental Panoramic Radiograph

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

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