Fig. 1. Local refinement diagram of HBS

Fig. 2. Flow chart of algorithm

Fig. 3. Segmentation and registration results of brain MRI and lung CT images. (a) Image to be registered; (b) image of target; (c) initial image difference; (d) segmented contours; (e)-(g) control grid; (h) forward deformed mesh; (i) backward deformed mesh; (j) result of segmentation; (k) result of registration; (l) image difference after registration

Fig. 4. Results of segmentation. (a) Target images without noise-free; (b) images to be registered; (c) segmentation result of images to be registered; (d)-(f) target images with noise intensities of 0.02, 0.05, and 0.1; (g)-(i) segmentation results of Fig. (d)-Fig. (f) by level set method; (j)-(l) segmentation results of Fig. (d)-Fig. (f) by proposed method

Fig. 5. Registration results of two methods. (a) Images to be registered; (b) images of target; (c) registration results of HBS; (d) final image differences of HBS; (e) registration results of DHBS; (f) final image differences of DHBS

Fig. 6. Results of registration. (a) Images to be registered; (b) images of target; (c) registration results of HBS; (d) registration results of joint method; (e) registration results of proposed method; (f) final image differences of HBS; (g) final image differences of joint method; (h) final image differences of proposed method

Fig. 7. Noise experiment comparison chart. (a) Images to be registered; (b) images of target; (c) registration results of joint method; (d) registration results of proposed method; (e) final image differences of joint method; (f) final image differences of proposed method

Table 1. DS value of two segmentation methods

Table 2. MSE and registration time of two registration methods

Table 3. MSE and registration time of three registration methods

Table 4. MSE and registration time under different noise intensities