An iterative possibilistic knowledge diffusion approach for blind medical image segmentation
作者：
Highlights：
• A novel region-growing segmentation method based on possibilistic theory is proposed.
• Region-growing process is iteratively performed at the possibilistic knowledge representation level.
• Possibility theory allows adequate semantic knowledge modeling without huge constraints.
• Validation is done in the context of pixel classification using both real and synthetic data.
• Proposed approach shows remarkable stable behaviour during quantitative assessment.
摘要
•A novel region-growing segmentation method based on possibilistic theory is proposed.•Region-growing process is iteratively performed at the possibilistic knowledge representation level.•Possibility theory allows adequate semantic knowledge modeling without huge constraints.•Validation is done in the context of pixel classification using both real and synthetic data.•Proposed approach shows remarkable stable behaviour during quantitative assessment.
论文关键词：Possibilistic knowledge representation,Knowledge diffusion modeling,Iterative segmentation,Region growing,Image segmentation,Mammographic medical images
论文评审过程：Received 5 May 2017, Revised 8 January 2018, Accepted 24 January 2018, Available online 31 January 2018, Version of Record 3 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.01.024
