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| Content Provider | IET Digital Library |
|---|---|
| Author | Tafsast, Abdelghani Hadjili, Mohamed Laid Bouakaz, Ayache Benoudjit, Nabil |
| Abstract | In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods. |
| Starting Page | 389 |
| Ending Page | 396 |
| Page Count | 8 |
| ISSN | 17519659 |
| Volume Number | 11 |
| e-ISSN | 17519667 |
| Issue Number | Issue 6, Jun (2017) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/11/6 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1024 |
| Journal | IET Image Processing |
| Publisher Date | 2017-03-03 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | 18-fluorodeoxyglucose Positron Emission Tomography 18F-FDG-PET Image Automatic Optimal Cluster Number Estimation Biological Tumour Volume Delineation Biology And Medical Computing BTV Delineation Computer Vision And Image Processing Technique Data Handling Technique Image Segmentation Intrinsic Positron Emission Tomography Image Information K-means Clustering Algorithm Laryngeal Tumour Biological Tumour Volume Segmentation Medical Image Processing Nuclear Medicine, Emission Tomography Optical, Image And Video Signal Processing Patient Clinical Dataset Patient Diagnostic Method And Instrumentation Pattern Clustering Positron Emission Tomography Radiotherapy Tumours Unsupervised Cluster-based Method |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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