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題名 | Using Maximal Cross-section Detection for the Registration of 3D Image Data of the Head= |
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作者 | Jiang, Ching-fen; Huang, Chih-hua; Yang, Shih-ting; |
期刊 | Journal of Medical and Biological Engineering |
出版日期 | 20110600 |
卷期 | 31:3 2011.06[民100.06] |
頁次 | 頁217-225 |
分類號 | 410.1644 |
語文 | eng |
關鍵詞 | Image segmentation; Maximal cross-section; 3D image registration; |
英文摘要 | An efficient and affordable method to register 3D image data from multiple imaging modalities is in great demand in clinical applications involving the evaluation of brain structural abnormalities associated with functional changes. Current auto-registration methods based on maximization of intensity similarity measures are inapplicable to clinical data with inconsistent target volumes. Registration methods for this purpose based on image segmentation are unfeasible for clinical use due to the complexity of brain structure and its individual-dependent variation with the course of diseases. To overcome the inconsistent target volume problem and make the task of feature identification easier, we propose a practical scheme wherein the maximal cross-sectional area (MCSA) of the head is considered as the main feature for 3D registration after re-alignment of the orientations of the vertical axes of the head volumes. The overall process is conceptually straightforward, and the automatic detection of MCSA can increase the registration reliability. The efficacy of using the MCSA as the feature for registration was verified qualitatively and quantitatively in two different cases. Comparisons of the results showed that using the MCSA to refine the registration method using an automatic method alone does actually increase registration accuracy. We then further demonstrated that the registration accuracy is optimal when using the MCSA as the registration feature. The results of this study suggest that MCSA can be a valuable anatomical feature for further development of advanced registration methods. |
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