Design and Analysis of an Efficient Evolutionary Image Segmentation Algorithm
Journal of VLSI Signal Processing Systems
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
Molecular image segmentation based on improved fuzzy clustering
Journal of Biomedical Imaging
Using fuzzy clustering methods for delineating urban housing submarkets
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A Modified Deterministic Annealing Algorithm for Robust Image Segmentation
Journal of Mathematical Imaging and Vision
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Pattern Recognition Letters
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Edge detection in digital images using fuzzy numbers
International Journal of Innovative Computing and Applications
A kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation
Journal of Computational Methods in Sciences and Engineering
Video sequence motion tracking by fuzzification techniques
Applied Soft Computing
A modified-FCM segmentation algorithm for brain MR images
Proceedings of the 2009 International Conference on Hybrid Information Technology
A segmentation method for images compressed by fuzzy transforms
Fuzzy Sets and Systems
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Fuzzy Systems
Cytoplasm image segmentation by spatial fuzzy clustering
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Unsupervised image segmentation using penalized fuzzy clustering algorithm
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Objective PET lesion segmentation using a spherical mean shift algorithm
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Spatial homogeneity-based fuzzy c-means algorithm for image segmentation
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
FCM with spatial and multiresolution constraints for image segmentation
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Improved Fuzzy Clustering Algorithms in Segmentation of DC-enhanced breast MRI
Journal of Medical Systems
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation
Computer Vision and Image Understanding
Fuzzy C-mean based brain MRI segmentation algorithms
Artificial Intelligence Review
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We present an adaptive fuzzy clustering scheme for image segmentation, the adaptive fuzzy clustering/segmentation (AFCS) algorithm. In AFCS, the nonstationary nature of images is taken into account by modifying the prototype vectors as functions of the sample location in the image. The inherent high interpixel correlation is modeled using neighborhood information. A multiresolution model is utilized for estimating the spatially varying prototype vectors for different window sizes. The fuzzy segmentations at different resolutions are combined using a data fusion process in order to compute the final fuzzy partition matrix. The results provide segmentations, having lower fuzzy entropy when compared to the possibilistic C-means algorithm, while maintaining the image's main characteristics. In addition, due to the neighborhood model, the effects of noise in the form of single pixel regions are minimized