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