An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm

作者：

Highlights：

• The novel gray-level co-occurrence matrix based cellular automata (GLCM-CA) for image transformation was proposed.

• We proposed Improved Tumor-Cut algorithm (ITC) to achieve the higher performance.

• State-of-the-art ITC and GLCM-CA were used for segmentation and evaluation.

• Dice quantitative evaluation metric was implemented on BRaTS2013 training and testing datasets.

摘要

•The novel gray-level co-occurrence matrix based cellular automata (GLCM-CA) for image transformation was proposed.•We proposed Improved Tumor-Cut algorithm (ITC) to achieve the higher performance.•State-of-the-art ITC and GLCM-CA were used for segmentation and evaluation.•Dice quantitative evaluation metric was implemented on BRaTS2013 training and testing datasets.

论文关键词：Gray-level co-occurrence matrix,Cellular automata,Tumor-cut segmentation,Spatial information

论文评审过程：Received 22 June 2016, Revised 16 October 2016, Accepted 16 October 2016, Available online 11 November 2016, Version of Record 2 January 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.064