Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin, Agus Zainal; Adam, Safri; Mohammad, A Hossain; Anggris, Fatoni; Indraswari, Rarasmaya; Navastara, Dini Adni

doi:10.22266/ijies2019.1231.07

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…Arifin's research conducted the detection of overlapping teeth as a first step to separating the two overlapping teeth [6]. Detection is done utilizing the intensity features of overlap objects by performing adapted automatic thresholding to get markers on the location of overlapping objects.…”

Section: Related Workmentioning

confidence: 99%

“…But the slope angle output determines the orientation of the object. The object is selected as vertical with the condition that it has a degree of slope < −° or > ° [6]. Objects that out of conditions will be eliminated.…”

Section: Overlap Object Segmentationmentioning

confidence: 99%

“…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%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

Section: Overlap Object Segmentationmentioning

confidence: 99%

Section: Evaluation Of Automatic Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Active Contour can be applied to medical images in very straightforward way, i.e. dental panoramic radiograph [15] and histophatological image [16]. One of the Active Contour with the popular Level Set model is the Chan-Vese Model (CV) [17].…”

Section: Introductionmentioning

confidence: 99%

A New Local Gaussian Variational Level Set for Globus Pallidus Segmentation

Setiawan¹,

Fatichah²,

Sarno³

2020

IJIES

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Globus Pallidus is an object in Magnetic Resonance Imaging (MRI) with severe intensity inhomogeneity and noises, such as small, low contrast, and weak boundary. Existing active contour methods fall into false boundaries while segmenting objects, which is smaller than the background like Globus Pallidus. This paper proposes a new local gaussian variational level set (NGVLS) for Globus Pallidus segmentation. We developed an energy term to create a smooth curve and prevent evolving curve into the false boundary. In the experiment, we compare NGVLS qualitatively and quantitively with existing methods such as Chan-Vese (CV), Region Scalable Fitting (RSF), Improved Region Scalable Fitting (Im-RSF), Local Pre-Fitting (LPF), and Local Gaussian Distribution Fitting (LGDF). We use Dice Similarity Coefficent (DSC) and Misclassification Error (ME) to measure the accuracy and error of the segmentation, respectively. The experiment is using 40 MRI datasets from Rumah Sakit National Hospital Surabaya, Indonesia. Qualitatively, the results show that NGVLS shows the best segmentation compared to the existing methods. Quantitatively, NGVLS achieves the highest average, minimum, and maximum value from DSC in 0.8291, 0.7119, and 0.9050, respectively. Also, NGVLS achieves the lowest average, minimum, and maximum value from ME in 0.0050, 0.0023, and 0.0155, respectively.

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