Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure

By Songcan Chen, Daoqiang Zhang. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004

1,182 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c6641719743d7a5ae0bc734ed43cdac3fc0c57df

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Comparative Analysis of K-Means and Fuzzy C- Means Algorithms (2013, methodology): “Infact, FCM is a data clustering technique [11, 7] in which a data set is grouped into n clusters with every data point in the dataset related to every cluster and it will have a high degree of belonging (connection) to that cluster and another data point that lies far away from the center of a clus”
A survey of kernel and spectral methods for clustering (2008, methodology): “[28] [29] [30] has been applied in image segmentation problems while in Ref.”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, background): “, 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].”
State of the art survey on MRI brain tumor segmentation. (2013, methodology): “Szilágyi et al. [75], and Chen and Zhang [76] proposed to evaluate the neighborhoods of each pixel as a pre-ﬁ ltering step, and perform FCM afterwards.”
Image segmentation by generalized hierarchical fuzzy C-means algorithm (2015, methodology): “We also evaluate GGFCM [2], MFCM [4], FCM S [6], FLICM [3], FRFCM [29] and HMRF-FCM [12] for experimental comparison.”
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (2007, methodology): “FCM_S1 not only considerably reduces the execution time for clustering an image but also improves the robustness to Gaussian noise [15].”
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation (2019, methodology): “The first group employs neighborhood information of a center pixel using a window of fixed size to improve image segmentation effect, e.g., FCM algorithm with spatial constraints (FCM_S) [16], FCM_S1 [17], FCM_S2 [17], fast generalized FCM algorithm (FGFCM) [18], fuzzy local information c-means clus”
A review of algorithms for medical image segmentation and their applications to the female pelvic cavity (2010, methodology): “2008 ) or using kernel techniques that can better transfer non-linear problems to linear problems (Zhang and Chen 2003 ; Liao et al.”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, positive): “And this method can also be used in clustering, as shown in support vector clustering [14] and kernel (fuzzy) c-means algorithms [9].”
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “Nine state-of-the-art clustering algorithms: FCM [17], FCM_S1 [19], FCM_S2 [19], EnFCM [20], FGFCM [21], FLICM [22], KWFLICM [24], NWFCM [28], and NDFCM [29], are employed in these experiments to compare with the proposed FRFCM.”