Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

By Weiling Cai, Songcan Chen, Daoqiang Zhang. Pattern Recognition, 2007

1,147 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5b2ea359d4b77c469443dd04f4440d6d436fedff

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Citations in the corpus

ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation (2015, methodology): “hi & Malik, 2000; Felzenszwalb & Huttenlocher, 2004; Boykov & Funka-Lea, 2006) that represent the pixels of the image as nodes of the graph, greedy approaches (such as, e.g., Besag, 1986; Cai et al., 2007) and continuation methods (such as, e.g., Blake & Zisserman, 1987; Geman & ”
Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds (2010, methodology): “to the fuzzy k-means (also called fuzzy c-means) classification, is therefore termed as robust fuzzy c-means method [34], [35].”
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation (2011, background): “One of the problems of standard FCM algorithms in an image segmentation is the lack of spatial information [4,5,9].”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, neutral): “Cai et al. [11] proposed the fast generalized FCM (FGFCM) algorithm.”
Medical Image Analysis using Convolutional Neural Networks: A Review (2017, methodology): “ [17], object matching [18] and object recognition [19]. In literature, several image segmentation algorithms have been proposed, which are based on thresholding [20], region growing [21], clustering [22], edge detection [23], active contour models [24], graph cut [25] and mean shift [26]. In recent”
Dual Fuzzy-Possibilistic Co-clustering for Document Categorization (2007, background): “can find a large number of FCM-variants in the literatures [2, 7].”
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”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “The clustering performance can also be enhanced by combining pixel-wise fuzzy classification with preprocessing (noise cleaning in the original image) [49, 78] and postprocessing (noise cleaning on the classified data) [78].”
Kvasir-SEG: A Segmented Polyp Dataset (2019, methodology): “several experiments, which we will described next. 5.1 Baseline Models As our baseline, we have conducted initial investigations using two dierent methods. The rst method is based on the ecient FCM [6] unsupervised clustering algorithm. The second method is based on the deep-learning ResUNet [28] ar”
Clustering Algorithms Research (2008, neutral): “2007 年,Cai 等人([27])结合局部空间和灰度信息,提出快速通用 FCM 聚类算法 FGFCM,其特点为:(1) 用一个”
ReSeg: A Recurrent Neural Network for Object Segmentation (2015, methodology): “…& Huttenlocher, 2004; Boykov & Funka-Lea, 2006) that represent the pixels of the image as nodes of the graph, greedy approaches (such as, e.g., Besag, 1986; Cai et al., 2007) and continuation methods (such as, e.g., Blake & Zisserman, 1987; Geman & Geman, 1984) are usually more popular.”
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”
A survey of MRI-based brain tumor segmentation methods (2014, methodology): “BCFCM algorithm provides good-quality segmented brain images in a very quick way, which makes it an excellent tool to support virtual brain endoscopy to realize the segmentation of brain tumor [71] .”