Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1217002(2022)
Liver Segmentation from CT Volumes Based on Spatial Fuzzy C-Means and Graph Cuts
Fig. 5. Segmentation result. (a) Without location information; (b) with location information
Fig. 6. Constrains estimation. (a) Result of previous segmentation; (b) image to be segmented; (c) image of distance transformation
Fig. 7. Comparison of segmentation results. (a) Without neighborhood pixels; (b) with neighborhood pixels
Fig. 8. Removal of inferior vena cava. (a) Result of segmentation; (b) result of erosion; (c) region of postcava; (d) result of postcava removal
Fig. 9. Result comparison of HLS, IRG, and proposed methods. (a) Results of HLS; (b) results of IRG; (c) results of proposed method
Fig. 10. Performance comparison of three methods over 20 CT volumes. Horizontal axis is number of CT volume and vertical axis are values of 5 evaluation methods, respectively. (a) VOE; (b) RVD; (c) ASD; (d) RMSD; (e) MSD
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Qing Yang, Yuqian Zhao, Fan Zhang, Miao Liao. Liver Segmentation from CT Volumes Based on Spatial Fuzzy C-Means and Graph Cuts[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1217002
Category: Medical Optics and Biotechnology
Received: Apr. 16, 2021
Accepted: Jun. 11, 2021
Published Online: May. 23, 2022
The Author Email: Yuqian Zhao (zyq@ceu.edu.cn)