An unsupervised orthogonal rotation invariant moment based fuzzy C-means approach for the segmentation of brain magnetic resonance images
作者：
Highlights：
• Local Zernike moment-based bias corrected fuzzy C-means method has been proposed.
• A simple regularization parameter free segmentation framework has been developed.
• The proposed method is robust to several anomalies present in the MR images.
• The proposed method preserves edges, corners, and fine structures of the image.
• Superior segmentation results are demonstrated through detailed experimentation.
摘要
•Local Zernike moment-based bias corrected fuzzy C-means method has been proposed.•A simple regularization parameter free segmentation framework has been developed.•The proposed method is robust to several anomalies present in the MR images.•The proposed method preserves edges, corners, and fine structures of the image.•Superior segmentation results are demonstrated through detailed experimentation.
论文关键词：MRI segmentation,Local Zernike moments,Rician noise,Intensity inhomogeneity,Bias correction
论文评审过程：Received 7 January 2020, Revised 7 September 2020, Accepted 7 September 2020, Available online 14 September 2020, Version of Record 12 October 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113989
