Yuexiang Shi, Cai Chen. Non-Rigid Registration Segmentation Algorithm Based on Optimal Atlas Multi-Model Image[J]. Acta Optica Sinica, 2019, 39(4): 0410002
- Acta Optica Sinica
- Vol. 39, Issue 4, 0410002 (2019)
Fig. 1. Framework of registration and segmentation
Fig. 2. Regional fusion segmentation of lung tissues. (a)-(c) Images to be fused; (d)-(f) fusion and segmentation images; (g)-(j) binary images of mask
Fig. 3. Atlas image matching based on optimal similarity
Fig. 4. Adaptive weighted meshing of locally B-spline. (a) 2D meshing; (b) adaptive mobile grid-model
Fig. 5. Optimization process of the iterative computation with L-BFGS
Fig. 6. Image registration-segmentation process and results based on different Atlas similarities. (a) 0.93; (b) 0.90; (c) 0.88; (d) 0.85
Fig. 7. MSE trend of image registration under different Atlas similarities. (a) 0.93; (b) 0.90; (c) 0.88; (d) 0.85
Fig. 8. Mask image and original mask effect under different Atlas similarities. (a) 0.93; (b) 0.90; (c) 0.88; (d) 0.85
Fig. 9. Detection of location of lobar fissures based on proposed registration-segmentation method. (a) Middle layer of lung; (b) upper layer of lung; (c) lower layer of lung
Fig. 10. Iterative optimization of registration evaluation criteria. (a) NMI and CC; (b) NMI and SSD; (c) SSD and CC; (d) ROI accuracy
Fig. 11. Optimizing results of average evaluation indicators. (a) Iterative optimization of NMI; (b) iterative optimization of SSD; (c) iterative optimization of CC; (d) iterative optimization of ROI accuracy
Fig. 12. Three-dimensional reconstruction model for segmentation of lung registration results. (a) Front view and (b) side view for the similarity of 0.90; (c) front view and (d) side view for the similarity of 0.93
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Table 1. Search results with optimal Atlas image strategy
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Table 2. Comparison of mean values of image registration data from different algorithms
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