A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
By Daoqiang Zhang, Songcan Chen. Artif. Intell. Medicine, 2004
607 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a358f6d55cd95a39741b5a24ce7098081836c52c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Sun-Yuan Kung
- Francesco Masulli
- Stefano Rovetta
- Maurizio Filippone
- Weiling Cai
- Francesco Camastra
- Songcan Chen
- Daoqiang Zhang
Citations in the corpus
- A survey of kernel and spectral methods for clustering (2008, methodology): “[28] [29] [30] has been applied in image segmentation problems while in Ref.”
- Review of brain MRI image segmentation methods (2010, unknown): “Many algorithms are introduced to make FCM robust against noise and in homogeneity but most of them still are not flawless (Hall et al. 1992; Lions et al. 1992; Acton and Mukherjee 2000; Zhang and Chen 2004; Dave 1991; Tolias and Panas 1998).”
- Prediction of Suspended Sediment Load Using Data-Driven Models (2019, methodology): “Finally, Bezdek et al. [38] proposed the FCM clustering method and has been enhanced by Zhang and Chen [39], which is considered as one of the most effective methods for data clustering.”
- Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (2007, methodology): “(16) is similar to the filtered result derived by optimizing a kernel-induced robust measure [ 25-26 ] based”
- Kernel Methods and Machine Learning (2014, methodology): “Many kernel-based K-means clustering methods have been proposed in the literature [31, 85, 305, 317, 319].”
- Edge Detection Techniques for Image Segmentation - A Survey of Soft Computing Approaches (2009, background): “Section III presents the conclusion for soft computing approaches to image segmentation.”
- MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization (2005, methodology): “In [18], a simulated -weighted image with 3% noise was segmented by four methods.”