A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons
The accurate location of the mid-sagittal plane is fundamental for the assessment of craniofacial dysmorphisms and for a proper corrective surgery planning. To date, these elaborations are carried out by skilled operators within specific software environments. Since the whole procedure is based on t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/2/245 |
| _version_ | 1797999139279077376 |
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| author | Luca Di Angelo Paolo Di Stefano Lapo Governi Antonio Marzola Yary Volpe |
| author_facet | Luca Di Angelo Paolo Di Stefano Lapo Governi Antonio Marzola Yary Volpe |
| author_sort | Luca Di Angelo |
| collection | DOAJ |
| description | The accurate location of the mid-sagittal plane is fundamental for the assessment of craniofacial dysmorphisms and for a proper corrective surgery planning. To date, these elaborations are carried out by skilled operators within specific software environments. Since the whole procedure is based on the manual selection of specific landmarks, it is time-consuming, and the results depend on the operators’ professional experience. This work aims to propose a new automatic and landmark-independent technique which is able to extract a reliable mid-sagittal plane from 3D CT images. The algorithm has been designed to perform a robust evaluation, also in the case of large defect areas. The presented method is an upgraded version of a mirroring-and registration technique for the automatic symmetry plane detection of 3D asymmetrically scanned human faces, previously published by the authors. With respect to the published algorithm, the improvements here introduced concern both the objective function formulation and the method used to minimize it. The automatic method here proposed has been verified in the analysis of real craniofacial skeletons also with large defects, and the results have been compared with other recent technologies. |
| first_indexed | 2024-04-11T10:59:50Z |
| format | Article |
| id | doaj.art-a0129030a9a34a5bbd2b1a108b9d8571 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T10:59:50Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-a0129030a9a34a5bbd2b1a108b9d85712022-12-22T04:28:40ZengMDPI AGSymmetry2073-89942019-02-0111224510.3390/sym11020245sym11020245A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial SkeletonsLuca Di Angelo0Paolo Di Stefano1Lapo Governi2Antonio Marzola3Yary Volpe4Department of Industrial and Information Engineering, and of Economics, University of L’Aquila, via G. Gronchi 18, 67100 L’Aquila, ItalyDepartment of Industrial and Information Engineering, and of Economics, University of L’Aquila, via G. Gronchi 18, 67100 L’Aquila, ItalyDepartment of Industrial Engineering, University of Florence, via di Santa Marta, 3, 50139 Firenze, ItalyDepartment of Industrial Engineering, University of Florence, via di Santa Marta, 3, 50139 Firenze, ItalyDepartment of Industrial Engineering, University of Florence, via di Santa Marta, 3, 50139 Firenze, ItalyThe accurate location of the mid-sagittal plane is fundamental for the assessment of craniofacial dysmorphisms and for a proper corrective surgery planning. To date, these elaborations are carried out by skilled operators within specific software environments. Since the whole procedure is based on the manual selection of specific landmarks, it is time-consuming, and the results depend on the operators’ professional experience. This work aims to propose a new automatic and landmark-independent technique which is able to extract a reliable mid-sagittal plane from 3D CT images. The algorithm has been designed to perform a robust evaluation, also in the case of large defect areas. The presented method is an upgraded version of a mirroring-and registration technique for the automatic symmetry plane detection of 3D asymmetrically scanned human faces, previously published by the authors. With respect to the published algorithm, the improvements here introduced concern both the objective function formulation and the method used to minimize it. The automatic method here proposed has been verified in the analysis of real craniofacial skeletons also with large defects, and the results have been compared with other recent technologies.https://www.mdpi.com/2073-8994/11/2/245feature recognitionmedical imagingsymmetry analysismid-sagittal planecranio-maxillofacial |
| spellingShingle | Luca Di Angelo Paolo Di Stefano Lapo Governi Antonio Marzola Yary Volpe A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons Symmetry feature recognition medical imaging symmetry analysis mid-sagittal plane cranio-maxillofacial |
| title | A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons |
| title_full | A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons |
| title_fullStr | A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons |
| title_full_unstemmed | A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons |
| title_short | A Robust and Automatic Method for the Best Symmetry Plane Detection of Craniofacial Skeletons |
| title_sort | robust and automatic method for the best symmetry plane detection of craniofacial skeletons |
| topic | feature recognition medical imaging symmetry analysis mid-sagittal plane cranio-maxillofacial |
| url | https://www.mdpi.com/2073-8994/11/2/245 |
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