A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

By Mohamed N. Ahmed, S. Yamany, N. Mohamed, Aly A. Farag, T. Moriarty. IEEE Transactions on Medical Imaging, 2002

2,021 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6534e7184ac8f3926b5c3f09da19d52601fb7418

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Citations in the corpus

MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization (2005, methodology): “Other improved versions of FCM by the modiﬁcation of the objective function were introduced by Ahmed et al. [17] and by Zhang and Chen [18].”
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (2007, methodology): “2, 8] with respect to the optimal segmentation accuracy (SA), where SA is defined as the sum of the total number of pixels divided by the sum of number of correctly classified pixels [14].”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, neutral): “Digital Object Identifier 10.1109/TIP.2012.2219547 To deal with the first problem, many improved FCM algorithms have been proposed by incorporating local spatial information into original FCM objective function [8]–[12].”
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering (2012, neutral): “Remote Sens., vol. 45, no. 5, pp. 1432–1445, May 2007”
Fuzzy c-means clustering with spatial information for image segmentation (2006, background): “Ahmed et al. [8] modiﬁed the objective function of the standard FCM algorithm to allow the labels in the immediate neighborhood of a pixel to inﬂuence its labeling.”
3D Deep Learning on Medical Images: A Review (2020, background): “MRI images are corrupted by a low-frequency and smooth bias field signal produced by MRI scanners, thereby affecting pixel intensities to fluctuate [51,52].”
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation (2019, methodology): “, FCM algorithm with spatial constraints (FCM_S) [16], FCM_S1 [17], FCM_S2”
A review of algorithms for medical image segmentation and their applications to the female pelvic cavity (2010, methodology): “The performance of FCM algorithms can be improved through adding spatial influence to the objective function (Pham and Prince 1999; Ahmed et al. 2002; Cai et al. 2007; Wang et al. 2008; Zhou et al. 2008) or using kernel techniques that can better transfer non-linear problems to linear problems (Zhan”
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation (2004, background): “[11] proposed an algorithm to compensate for the intensity inhomogeneity and to label a pixel by considering its immediate neighborhood.”
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure (2004, methodology): “…and while many other algorithms have also been proposed to deal with the second problem by incorporating spatial information into original FCM objective function [5]–[10],[33], e.g., Ahmed et al. modified FCM objective function by incorporating spatial constraints (called as FCM_S later) [9].”
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “[18] proposed FCM algorithm with 1063-6706 © 2018 IEEE.”
Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm (2006, background): “There has also been an increasing interest in applying soft segmentation algorithms, where a pixel may be classified partially into multiple classes, for MR images segmentation [5], [6], [7].”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “, intensity values) of the neighboring pixels into the modified FCM objective function [77, 79] or into a similarity measure between cluster centers and image elements [80].”
Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “pixels problems; however, it has not been commonly applied in practical terms because it is not easy to use compared to other classification methods [68,72].”
Automated medical image segmentation techniques (2010, methodology): “[66,67] Further, for this segmentation, techniques based on statistical methods[68,69] and fuzzy methods,[58,70-73] which gives soft segmentation results are particularly useful in overcoming the above mentioned limitation.”