Fuzzy c-means clustering with weighted image patch for image segmentation

Authors:
Zexuan Ji;Yong Xia;Qiang Chen;Quansen Sun;Deshen Xia;David Dagan Feng
Affiliations:
Nanjing University of Science and Technology School of Computer Science and Technology, Nanjing 210094, China;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, NSW 2006, Australia and School of Computer Science, Northweste ...;Nanjing University of Science and Technology School of Computer Science and Technology, Nanjing 210094, China;Nanjing University of Science and Technology School of Computer Science and Technology, Nanjing 210094, China;Nanjing University of Science and Technology School of Computer Science and Technology, Nanjing 210094, China;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, NSW 2006, Australia and Med-X Research Institute, Shanghai Jia ...
Venue:
Applied Soft Computing
Year:
2012

Citing 10
Cited 3

An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
A Non-Local Algorithm for Image Denoising

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Pattern Recognition
Novel modified fuzzy c-means algorithm with applications

Digital Signal Processing
A robust and fast non-local means algorithm for image denoising

Journal of Computer Science and Technology
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
Universal discrete denoising: known channel

IEEE Transactions on Information Theory
A modified fuzzy C-means algorithm for MR brain image segmentation

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition

Color image segmentation using adaptive unsupervised clustering approach

Applied Soft Computing
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

Computer Methods and Programs in Biomedicine
Linguistic description about circular structures of the Mars' surface

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FCM (WIPFCM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFCM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations.