An Automatic Segmentation and Reconstruction of Mandibular Structures from CT-Data

Barandiarán, Íñigo; Macía, Iván; Berckmann, Eva; Wald, Diana; Dupillier, Michael Pierre; Paloc, Céline; Graña, Manuel

doi:10.1007/978-3-642-04394-9_79

Cited by 24 publications

(17 citation statements)

References 6 publications

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“…Otsu's multi-thresholding is used in [11] for separate the bone element on CBCT image. According to Kurita, et al (1992) the Otsu's thresholding method is best used on histogram consist of Gaussian distributions with uniform variance [24].…”

Section: Discussionmentioning

confidence: 99%

“…This approach has been utilized by Hassan, et al (2010) on his research about influence of scanning and reconstruction on quality of 3D surface model on CBCT [10]. Barandiaran, et al (2009) use multi-thresholding method by Otsu to separate the input CBCT images into three classes, one of them representing the mandibular structure [11]. However, the resulting segmented images contains not only the bone but also the teeth element.…”

Section: Introductionmentioning

confidence: 99%

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

Indraswari¹,

Arifin²,

Suciati³

et al. 2019

IJIES

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Automatic segmentation of mandibular cortical bone is challenging due to the appearance of teeth that have similar intensity with the bone tissue and the variety of bone intensity. In this paper we propose a new method for automatic segmentation of mandibular cortical bone on cone-beam computed tomography (CBCT) images. The bone tissue is segmented by using Gaussian mixture model for histogram thresholding. The mandibular inferior cortical bone is obtained by incorporating several polynomial models to fit the structure of cortical bone on coronal slices. The buccal and lingual cortical plate is separated by using histogram thresholding for teeth elimination and polynomial fitting for shape extraction. After performing 3D reconstruction, the volumetric cortical bone is obtained. The proposed method gives average accuracy, sensitivity, and specificity value of 96.82%, 85.96%, 97.60%, respectively. This shows that the proposed method is promising for automatic and accurate segmentation of mandibular cortical bone on CBCT images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Indraswari¹,

Arifin²,

Suciati³

et al. 2019

IJIES

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show abstract

“…3,4 However, the intensity integral and projection can only be used to find coarse tooth segmentation, e.g., the teeth separating planes. The multiple-threshold method by Otsu together with region growing was used for 3D reconstruction, [5][6][7] where image-and mesh-oriented filters were applied for mesh smoothing. The tooth boundary was initialized by a threshold and computed based on the intensity and shape information on the previous slice.…”

Section: Introductionmentioning

confidence: 99%

3D exemplar‐based random walks for tooth segmentation from cone‐beam computed tomography images

Pei

Zha

et al. 2016

Medical Physics

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“…The maxilla exhibits thin bony structures (palate, sinus maxillaris, orbital walls), which are difficult to detect with intensity thresholds alone [5,6]. To the best of our knowledge, only Kainmueller et al [7] segment the maxillary bone as part of the midface.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Teeth in CT Data

Lamecker

Kainmueller

Zachow

2012

Lecture Notes in Computer Science

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Abstract. We propose a fully automatic method for tooth detection and classification in CT or cone-beam CT image data. First we compute an accurate segmentation of the maxilla bone. Based on this segmentation, our method computes a complete and optimal separation of the row of teeth into 16 subregions and classifies the resulting regions as existing or missing teeth. This serves as a prerequisite for further individual tooth segmentation. We show the robustness of our approach by providing extensive validation on 43 clinical head CT scans.

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An Automatic Segmentation and Reconstruction of Mandibular Structures from CT-Data

Cited by 24 publications

References 6 publications

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

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

3D exemplar‐based random walks for tooth segmentation from cone‐beam computed tomography images

Automatic Detection and Classification of Teeth in CT Data

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