scholarly journals A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Wenyuan Zhang ◽  
Tianyu Huang ◽  
Jun Chen
Keyword(s):  
Bias Correction ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Similarity Measures ◽  
Fuzzy Classification ◽  
Local Similarity ◽  
Real Image ◽  
Fcm Algorithm ◽  
Local Spatial Information ◽  
Image Details

This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.

scholarly journals Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 753
Author(s):  
Wenyuan Zhang ◽  
Xijuan Guo ◽  
Tianyu Huang ◽  
Jiale Liu ◽  
Jun Chen
Keyword(s):  
Bias Correction ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Similarity Measures ◽  
Distance Measures ◽  
Background Information ◽  
Local Similarity ◽  
Original Algorithm ◽  
Fcm Clustering

The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.

scholarly journals Noise Adaptive FCM algorithm for Segmentation of MRI Brain Images

2018 ◽  
Vol 8 (04) ◽  
Author(s):  
Harmanpreet Singh ◽  
Ramandeep Kaur
Keyword(s):  
Spatial Information ◽  
Brain Mri ◽  
Local Information ◽  
Brain Images ◽  
Fcm Algorithm ◽  
Non Local ◽  
Robust To Noise ◽  
Local Spatial Information ◽  
Mri Brain Images ◽  
Crucial Parameter

Image segmentation is an important task in many image processing applications. Fuzzy C means algorithm has been widely used for the segmentation. There are many versions of the traditional FCM algorithm which uses the local spatial information to increase the robustness of this algorithm in presence of noise, but all these algorithms do not successfully segment the images contaminated by heavy noise. In order to solve this problem, non-local spatial information present in the image is utilized. The filtering parameter ‘h’ in the non-local information is a crucial parameter which needs to be appropriately determined, irrespective of using a single constant value of ‘h’; we can determine its value using the standard deviation of noise present in the image. The adaptive non-local information determined is termed as noise adaptive non-local spatial information. This adaptive non-local information is used in the FCM algorithm for the segmentation of MRI images. In this paper Noise adaptive FCM algorithm (NAFCM) using adaptive non-local information is proposed. Therefore the proposed algorithm utilizes adaptive non-local information making it robust in presence of noise as well as preserving the details present in the image. The efficiency of the proposed algorithm is demonstrated by validation studies on synthetic as well as simulated brain MRI images. The results of the proposed algorithm show that the proposed algorithm is robust to noise and as compared to other state of the art algorithms.

scholarly journals Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
N. Vanello ◽  
E. Ricciardi ◽  
L. Landini
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Similarity Measures ◽  
Real Data ◽  
Fmri Data ◽  
Order Selection ◽  
Functional Magnetic Resonance ◽  
Model Order Selection ◽  
Model Order ◽  
Independent Components

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.

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