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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Keatmanee, C. Makhanov, S.S. Kotani, K. Kondo, T. Thongvigitmanee, S.S. |
| Copyright Year | 2015 |
| Description | Author affiliation: Sirindhorn Int. Inst. of Technol. (SIIT), PathumThani, Thailand (Keatmanee, C.; Makhanov, S.S.; Kondo, T.) || Nat. Electron. & Comput. Technol. Center (NECTEC), Pathumthani, Thailand (Thongvigitmanee, S.S.) || Japan Adv. Inst. of Sci. & Technol. (JAIST), Ishikawa, Japan (Kotani, K.) |
| Abstract | The success of dental implant surgery is subject to accurate advanced planning. In order to properly plan for suitable implant placement, it is necessary for accurate segmentation of the inferior alveolar canal. This paper presents a new approach of a semi-automatic method based on a new and effective active contour model viz. an adaptive diffusion flow active contour model. Cone-beam computed tomography (CBCT) images is used as a dataset to extract different views of an inferior alveolar canal. The method has been tested in a ground truth set and evaluated using three similar indicators (the Jaccard index, Dice's coefficient, and Overlap coefficient), achieving promising results in all of them (0.908±0.016, 0.947±0.008, and 0.954±0.008, respectively). Moreover, the presented results show that our method obtains higher accuracy values when compared with GVF snake. The method has proven to be significantly accurate and is possibly integrable in current dental implant surgery planning systems. |
| Starting Page | 57 |
| Ending Page | 60 |
| File Size | 481833 |
| Page Count | 4 |
| File Format | |
| ISBN | 9784901122146 |
| DOI | 10.1109/MVA.2015.7153132 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-18 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | MVA organization |
| Subject Keyword | Image segmentation Irrigation Three-dimensional displays Computed tomography Force Dentistry Active contours |
| Content Type | Text |
| Resource Type | Article |
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