Journal ArticleDOI
Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation
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TLDR
A novel clustering algorithm, namely generalized rough intutionistic fuzzy c-means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function.Abstract:
Intuitionistic fuzzy sets (IFSs), rough sets are efficient tools to handle uncertainty and vagueness present in images and recently are combined to segment medical images in the presence of noise and intensity non homogeneity (INU). These hybrid algorithms are sensitive to initial centroids, parameter tuning and dependency with the fuzzy membership function to define the IFS. In this paper, a novel clustering algorithm, namely generalized rough intutionistic fuzzy c-means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function. In this algorithm, each pixel is categorized into three rough regions based on the thresholds obtained by the image data by minimizing the noise. These regions are used to create IFS. The distance measure based on IFS eliminate's the influence of noise and INU present in the image producing accurate brain tissue segmentation. The proposed algorithm is evaluated through simulation and compared it with existing k-means (KM), fuzzy c-means (FCM), Rough fuzzy c-means (RFCM), Generalized rough fuzzy c-means (GRFCM), soft rough fuzzy c-means (SRFCM) and rough intuitionistic fuzzy c-means (RIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.read more
Citations
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Journal ArticleDOI
Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set
TL;DR: The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region, and the experimental results prove that the algorithm has strong anti-noise ability.
Journal ArticleDOI
Domain-independent severely noisy image segmentation via adaptive wavelet shrinkage using particle swarm optimization and fuzzy C-means
TL;DR: A Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation that helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties.
Journal ArticleDOI
An Analytical Review on Rough Set Based Image Clustering
TL;DR: The key issues which are involved during the development of rough set based clustering models are investigated in this paper and the measures of similarity as well as the evaluation criteria for rough clustering are discussed in this study.
Journal ArticleDOI
A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation
Jianhua Song,Zhe Zhang +1 more
TL;DR: A modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.
Proceedings ArticleDOI
Performance comparison and analysis of medical image segmentation techniques
TL;DR: In this article, the performance analysis and comparison of medical image segmentation techniques for surgical application is presented, where the authors have studied 500 CT images for performance analysis of the algorithms.
References
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Journal ArticleDOI
Intuitionistic fuzzy sets
TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.
Journal ArticleDOI
FCM: The fuzzy c-means clustering algorithm
TL;DR: A FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program is transmitted, which generates fuzzy partitions and prototypes for any set of numerical data.
Journal ArticleDOI
Rough fuzzy sets and fuzzy rough sets
Didier Dubois,Henri Prade +1 more
TL;DR: It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.
Journal ArticleDOI
Fuzzy c-means clustering with spatial information for image segmentation.
Keh-Shih Chuang,Hong Long Tzeng,Hong Long Tzeng,Sharon C.-A. Chen,Jay Wu,Jay Wu,Tzong-Jer Chen +6 more
TL;DR: This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.
Journal ArticleDOI
A possibilistic fuzzy c-means clustering algorithm
TL;DR: A new model called possibilistic-fuzzy c-means (PFCM) model, which solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM.