An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

Wang, Zhimin; Song, Qing; Soh, Yeng Chai; Sim, Kang

doi:10.1016/j.cviu.2013.05.001

Cited by 99 publications

(60 citation statements)

References 18 publications

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“…Based on information theoretic fuzzy clustering, a factor (e k ) is multiplied by above objective function (Relation 5) to eliminate noisy samples Wang et al (2013). e k acts an extra weighting factor or typical value to eliminate the outliers.…”

Section: Information Theoretic Fuzzy Clusteringmentioning

confidence: 99%

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Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory

Bakhshali

2016

Soft Comput

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In recent decades, fuzzy segmentation methods, FCM algorithm in particular, have been widely employed for medical image segmentation, because they can save more information of the original image. But some artifacts like spatial noise and bias in medical images disturb segmentation results. This research presents an improved and robust FCM method based on information theoretic clustering, which estimated and corrected the heterogeneity of the magnetic field image (bias) and minimized the noise effects. To increase accuracy against any noise, the mutual information between data distribution of each cluster and those out of that cluster were maximized. The simulation results of the proposed algorithm are compared with previous fuzzy segmentation methods and its superiority in terms of segmentation of MR images in the database of brain images and synthetic images is illustrated.

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Section: Information Theoretic Fuzzy Clusteringmentioning

confidence: 99%

“…The information theoretic criteria are based on a Renyi's entropy estimator. In Wang et al (2009Wang et al ( , 2013, a novel method for using spatial information based on FCM, was proposed. This method has robust and accurate segmentation in case of mixed noise.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory

Bakhshali

2016

Soft Comput

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“…And the objective function of fuzzy C average value clustering algorithm is listed as follows [4]: (4) where w denotes the index weight of fuzzy degree affecting the membership degree matrix.…”

Section: Basic Procedures Of Fuzzy Clustering Algorithmmentioning

confidence: 99%

Effect of Nutrition Method on Physical Recovery and Improvement of Basketball Player Based on Fuzzy Clustering Algorithm

Wu¹,

Chunfeng²

2017

IJHIT

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“…According to the source [3 ], most approaches are based on the similarities and differences and in particular can be divided into the following categories: getting threshold, clustering, edge detection and region extraction.…”

Section: -Fuzzy Segmentation Of the Imagesmentioning

confidence: 99%

Survey Article About Image Fuzzy Processing Algorithms

Hashemi¹,

Zangian²,

Shakeri³

et al. 2014

J. Math. Computer Sci.

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An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation

Cited by 99 publications

References 18 publications

Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory

Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory

Effect of Nutrition Method on Physical Recovery and Improvement of Basketball Player Based on Fuzzy Clustering Algorithm

Survey Article About Image Fuzzy Processing Algorithms

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