Level set framework based on local scalable Gaussian distribution and adaptive-scale operator for accurate image segmentation and correction
作者：
Highlights：
• The Gaussian function containing the mean and variance can better fit the local information.
• The image with uneven intensity can be corrected by introducing bias field.
• The split Bregman can effectively improve the efficiency of image segmentation.
• The multi-scale model reduces the complexity of adjusting parameters and improves efficiency.
• The color model extends the application scenarios of the model.
摘要
•The Gaussian function containing the mean and variance can better fit the local information.•The image with uneven intensity can be corrected by introducing bias field.•The split Bregman can effectively improve the efficiency of image segmentation.•The multi-scale model reduces the complexity of adjusting parameters and improves efficiency.•The color model extends the application scenarios of the model.
论文关键词：Image segmentation,Active contours,Split Bregman method,Bias correction,Gaussian distribution,Level set method,Adaptive-scale operator
论文评审过程：Received 11 May 2021, Revised 10 December 2021, Accepted 25 January 2022, Available online 16 February 2022, Version of Record 27 February 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116653
