Image segmentation with arbitrary noise models by solving minimal surface problems
作者：
Highlights：
• Variational segmentation formulation for images perturbed by arbitrary noise without assumptions on underlying noise model.
• Efficient numerical minimization based on total variation denoising computes all minimal surface solutions at once.
• Guaranteed existence of unique global minimizers and thus independence of initialization and avoidance of local minima.
• Evaluation of three different histogram-based thresholding techniques.
• Interactive minimal surface segmentation possible due to independence of the real-time thresholding step.
摘要
Highlights•Variational segmentation formulation for images perturbed by arbitrary noise without assumptions on underlying noise model.•Efficient numerical minimization based on total variation denoising computes all minimal surface solutions at once.•Guaranteed existence of unique global minimizers and thus independence of initialization and avoidance of local minima.•Evaluation of three different histogram-based thresholding techniques.•Interactive minimal surface segmentation possible due to independence of the real-time thresholding step.
论文关键词：Segmentation,Minimal surface problem,Thresholding,Chan–Vese model,Level set methods,Convex optimization,Physical noise
论文评审过程：Received 14 February 2014, Revised 12 January 2015, Accepted 14 January 2015, Available online 28 January 2015, Version of Record 16 July 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.01.006
