The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
컴퓨터 단층촬영(CT) 이미지에서 치아 분할은 다양한 컴퓨터 지원 절차에서 중요하고 어려운 작업입니다. 이 논문에서는 치아와 턱에 대한 이전 경험과 해부학적 지식에서 영감을 받아 CT 체적 데이터 세트에서 치아를 정량화하는 하이브리드 방법을 소개했습니다. 이에 우리는 적응형 임계화, 형태학적 연산, 파노라마 재샘플링 및 변형 수준 설정 알고리즘을 사용하는 새로운 분할 기술을 제안합니다. 제안된 방법은 다음과 같은 여러 단계로 구성된다. 먼저 CT 슬라이스에서 동작 영역을 결정한다. 둘째, 3D 펄스 결합 신경망(PCNN)을 기반으로 한 적응형 임계값 기술을 활용하여 뼈 조직을 다른 조직과 분리합니다. 셋째, 턱뼈의 치아에 대한 해부학적 지식과 파노렉스 라인(panorex line)을 이용하여 치아 조직을 다른 뼈 조직과 분류한다. 이 경우 panorex 선은 Otsu 임계값 지정 및 수학적 형태 연산자를 사용하여 추정됩니다. 그런 다음 제안된 방법에 따라 파노렉스 라인에 해당하는 직교 라인을 계산하고 데이터 세트의 파노라마 재샘플링을 수행합니다. 위턱과 아래턱의 분리 및 치아의 초기 분할은 파노라마 데이터세트의 통합 투영을 사용하여 수행됩니다. 위에서 언급한 절차를 기반으로 각 치아에 대한 초기 마스크가 얻어집니다. 마지막으로 치아의 초기 마스크를 활용하고 초기 치아 경계를 최종 윤곽으로 다듬기 위해 설정된 변형 수준을 적용합니다. 마지막 단계에서는 MC(마칭 큐브)로 알려진 표면 렌더링 알고리즘이 체적 시각화에 적용됩니다. 제안된 알고리즘은 30가지 경우를 대상으로 평가되었다. 분할된 이미지를 수동으로 윤곽선을 그린 윤곽과 비교했습니다. 임계값, 유역 및 이전 연구의 ROC 분석을 사용하여 분할 방법의 성능을 비교했습니다. 제안한 방법이 가장 잘 수행되었습니다. 또한 우리의 알고리즘은 이전 연구에 비해 속도가 빠르다는 장점이 있습니다.
Mohammad HOSNTALAB
Reza AGHAEIZADEH ZOROOFI
Ali ABBASPOUR TEHRANI-FARD
Gholamreza SHIRANI
Mohammad REZA ASHARIF
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Mohammad HOSNTALAB, Reza AGHAEIZADEH ZOROOFI, Ali ABBASPOUR TEHRANI-FARD, Gholamreza SHIRANI, Mohammad REZA ASHARIF, "A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 10, pp. 2137-2151, October 2009, doi: 10.1587/transinf.E92.D.2137.
Abstract: Teeth segmentation in computed tomography (CT) images is a major and challenging task for various computer assisted procedures. In this paper, we introduced a hybrid method for quantification of teeth in CT volumetric dataset inspired by our previous experiences and anatomical knowledge of teeth and jaws. In this regard, we propose a novel segmentation technique using an adaptive thresholding, morphological operations, panoramic re-sampling and variational level set algorithm. The proposed method consists of several steps as follows: first, we determine the operation region in CT slices. Second, the bony tissues are separated from other tissues by utilizing an adaptive thresholding technique based on the 3D pulses coupled neural networks (PCNN). Third, teeth tissue is classified from other bony tissues by employing panorex lines and anatomical knowledge of teeth in the jaws. In this case, the panorex lines are estimated using Otsu thresholding and mathematical morphology operators. Then, the proposed method is followed by calculating the orthogonal lines corresponding to panorex lines and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the integral projections of the panoramic dataset. 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 contour. In the last step a surface rendering algorithm known as marching cubes (MC) is applied to volumetric visualization. The proposed algorithm was evaluated in the presence of 30 cases. Segmented images were compared with manually outlined contours. We compared the performance of segmentation method using ROC analysis of the thresholding, watershed and our previous works. The proposed method performed best. Also, our algorithm has the advantage of high speed compared to our previous works.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2137/_p
부
@ARTICLE{e92-d_10_2137,
author={Mohammad HOSNTALAB, Reza AGHAEIZADEH ZOROOFI, Ali ABBASPOUR TEHRANI-FARD, Gholamreza SHIRANI, Mohammad REZA ASHARIF, },
journal={IEICE TRANSACTIONS on Information},
title={A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures},
year={2009},
volume={E92-D},
number={10},
pages={2137-2151},
abstract={Teeth segmentation in computed tomography (CT) images is a major and challenging task for various computer assisted procedures. In this paper, we introduced a hybrid method for quantification of teeth in CT volumetric dataset inspired by our previous experiences and anatomical knowledge of teeth and jaws. In this regard, we propose a novel segmentation technique using an adaptive thresholding, morphological operations, panoramic re-sampling and variational level set algorithm. The proposed method consists of several steps as follows: first, we determine the operation region in CT slices. Second, the bony tissues are separated from other tissues by utilizing an adaptive thresholding technique based on the 3D pulses coupled neural networks (PCNN). Third, teeth tissue is classified from other bony tissues by employing panorex lines and anatomical knowledge of teeth in the jaws. In this case, the panorex lines are estimated using Otsu thresholding and mathematical morphology operators. Then, the proposed method is followed by calculating the orthogonal lines corresponding to panorex lines and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the integral projections of the panoramic dataset. 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 contour. In the last step a surface rendering algorithm known as marching cubes (MC) is applied to volumetric visualization. The proposed algorithm was evaluated in the presence of 30 cases. Segmented images were compared with manually outlined contours. We compared the performance of segmentation method using ROC analysis of the thresholding, watershed and our previous works. The proposed method performed best. Also, our algorithm has the advantage of high speed compared to our previous works.},
keywords={},
doi={10.1587/transinf.E92.D.2137},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures
T2 - IEICE TRANSACTIONS on Information
SP - 2137
EP - 2151
AU - Mohammad HOSNTALAB
AU - Reza AGHAEIZADEH ZOROOFI
AU - Ali ABBASPOUR TEHRANI-FARD
AU - Gholamreza SHIRANI
AU - Mohammad REZA ASHARIF
PY - 2009
DO - 10.1587/transinf.E92.D.2137
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2009
AB - Teeth segmentation in computed tomography (CT) images is a major and challenging task for various computer assisted procedures. In this paper, we introduced a hybrid method for quantification of teeth in CT volumetric dataset inspired by our previous experiences and anatomical knowledge of teeth and jaws. In this regard, we propose a novel segmentation technique using an adaptive thresholding, morphological operations, panoramic re-sampling and variational level set algorithm. The proposed method consists of several steps as follows: first, we determine the operation region in CT slices. Second, the bony tissues are separated from other tissues by utilizing an adaptive thresholding technique based on the 3D pulses coupled neural networks (PCNN). Third, teeth tissue is classified from other bony tissues by employing panorex lines and anatomical knowledge of teeth in the jaws. In this case, the panorex lines are estimated using Otsu thresholding and mathematical morphology operators. Then, the proposed method is followed by calculating the orthogonal lines corresponding to panorex lines and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the integral projections of the panoramic dataset. 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 contour. In the last step a surface rendering algorithm known as marching cubes (MC) is applied to volumetric visualization. The proposed algorithm was evaluated in the presence of 30 cases. Segmented images were compared with manually outlined contours. We compared the performance of segmentation method using ROC analysis of the thresholding, watershed and our previous works. The proposed method performed best. Also, our algorithm has the advantage of high speed compared to our previous works.
ER -