C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation

Gharieb, R.R.; Gendy, Garas; Abdelfattah, Ahmed

doi:10.1007/s11760-016-0992-4

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…In [18], an approach to incorporating local spatial membership information into HCM algorithm has been presented. By adding Kullback-Leibler (KL) divergence between the membership function of an entity and the locally-smoothed membership in the immediate spatial neighborhood, the modified objective function, called the local membership KL divergencebased FCM (LMKLFCM), is given by [18][19][20][21][22].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…where γ is a weighting parameter experimentally selected to control the fuzziness induced by the second term in (19), u in ¼ 1 À u in is the complement of the membership function u in , π in and π in are the spatial local or moving averages of membership u in and the complement membership u in , functions respectively. These local membership and membership complement averages are computed by [18][19][20][21][22].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…The minimization of the objective function J LMKLFCM in (19) yields u in and v i to be given, respectively, by [18].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…To incorporate local spatial data into the LMKLFCM objective function in (19), the following objective function has been proposed in [18].…”

Section: Hcm Incorporating Local Data and Membership Kl Divergencementioning

confidence: 99%

“…In this chapter, HCM clustering algorithm is modified by incorporating local spatial data and Kullback-Leibler (KL) membership divergence [18][19][20][21][22]. The local data information is incorporated via an additional weighted HCM function in which the smoothed image data is used for the distance computation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Gharieb¹

2018

Recent Applications in Data Clustering

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Hard C-means (HCM) and fuzzy C-means (FCM) algorithms are among the most popular ones for data clustering including image data. The HCM algorithm offers each data entity with a cluster membership of 0 or 1. This implies that the entity will be assigned to only one cluster. On the contrary, the FCM algorithm provides an entity with a membership value between 0 and 1, which means that the entity may belong to all clusters but with different membership values. The main disadvantage of both HCM and FCM algorithms is that they cluster an entity based on only its self-features and do not incorporate the influence of the entity's neighborhoods, which makes clustering prone to additive noise. In this chapter, Kullback-Leibler (KL) membership divergence is incorporated into the HCM for image data clustering. This HCM-KL-based clustering algorithm provides twofold advantage. The first one is that it offers a fuzzification approach to the HCM clustering algorithm. The second one is that by incorporating a local spatial membership function into the HCM objective function, additive noise can be tolerated. Also spatial data is incorporated for more noise-robust clustering.

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Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…The minimization of the objective function J LMKLFCM in (19) yields u in and v i to be given, respectively, by [18].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…To incorporate local spatial data into the LMKLFCM objective function in (19), the following objective function has been proposed in [18].…”

Section: Hcm Incorporating Local Data and Membership Kl Divergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Gharieb¹

2018

Recent Applications in Data Clustering

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Influence of Rician Noise on Cardiac MR Image Segmentation Using Deep Learning

Wu,

Hsu,

Wang

et al. 2024

Intelligent Systems Design and Applications

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A Novel Entropy-Based FCM Algorithm Using Inverse Fuzzy Membership Framework and Uncertainty Measure for Segmentation of Brain MR Images

Ray¹,

Mahata

Sing

2022

Algorithms for Intelligent Systems

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C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation

Cited by 19 publications

References 17 publications

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Influence of Rician Noise on Cardiac MR Image Segmentation Using Deep Learning

A Novel Entropy-Based FCM Algorithm Using Inverse Fuzzy Membership Framework and Uncertainty Measure for Segmentation of Brain MR Images

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