Level set formulation for automatic medical image segmentation based on fuzzy clustering
作者：
Highlights：
• An automatic medical image segmentation method based on fuzzy clustering was proposed in this paper.
• A dynamic constrained term was added into the new energy functional, which can help us get more accurate results.
• Quantitative results such as DICE values, precision, f-measure and computation times are presented.
• Comparative results with other level set methods and the U-Net method show excellent performance of our model.
• We also discussed the sensitivities of parameters in the new dynamic constrained term, which show that it is robust.
摘要
•An automatic medical image segmentation method based on fuzzy clustering was proposed in this paper.•A dynamic constrained term was added into the new energy functional, which can help us get more accurate results.•Quantitative results such as DICE values, precision, f-measure and computation times are presented.•Comparative results with other level set methods and the U-Net method show excellent performance of our model.•We also discussed the sensitivities of parameters in the new dynamic constrained term, which show that it is robust.
论文关键词：Medical image segmentation,Fuzzy clustering,split Bregman method,Level set method,Dynamic constrained term
论文评审过程：Received 1 September 2019, Revised 20 March 2020, Accepted 7 June 2020, Available online 15 June 2020, Version of Record 16 June 2020.
论文官网地址：https://doi.org/10.1016/j.image.2020.115907
