A Robust Kernel-Based Fuzzy C-Means Algorithm by Incorporating Suppressed and Magnified Membership for MRI Image Segmentation

Tsai, Hsu-Shen; Hung, Wen-Liang; Yang, Miin-Shen

doi:10.1007/978-3-642-33478-8_92

Cited by 4 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there are some application papers [15,16,17,24,27,36] in the literature that give recipes for the choice of the suppression parameter.…”

Section: The Relation Between S-fcm and Gifp-fcm Clustering Modelsmentioning

confidence: 99%

“…(30). Tsai et al [36] also introduced a kernel-based suppressed FCM version, using the suppression formula derived from Eq. (29).…”

Section: Time Variant Suppression Rate Based On Current Cluster Protomentioning

confidence: 99%

“…For example, Szilágyi et al [28] explained the effect of the partition suppression in a comparative study with competitive learning [19], based on which later they introduced several generalized suppression rules for the fuzzy partition [30]. Several other works [15,16,17,24,27,36] proposed minor modifications of the original s-FCM algorithm, providing parameter selection schemes for the suppression rate, and successfully applying s-FCM in various image processing tasks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A review on suppressed fuzzy c-means clustering models

Szilágyi

Lefkovits

Iclănzan

2020

Acta Universitatis Sapientiae, Informatica

View full text Add to dashboard Cite

Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker convergence of the former and the finer partition quality of the latter. In the meantime, it became much more than that. Its competitive behavior was revealed, based on which it received two generalization schemes. It was found a close relative of the so-called fuzzy c-means algorithm with generalized improved partition, which could improve its popularity due to the existence of an objective function it optimizes. Using certain suppression rules, it was found more accurate and efficient than the conventional fuzzy c-means in several, mostly image processing applications. This paper reviews the most relevant extensions and generalizations added to the theory of fuzzy c-means clustering models with suppressed partitions, and summarizes the practical advances these algorithms can offer.

show abstract

“…However, there are some application papers [15,16,17,24,27,36] in the literature that give recipes for the choice of the suppression parameter.…”

Section: The Relation Between S-fcm and Gifp-fcm Clustering Modelsmentioning

confidence: 99%

“…(30). Tsai et al [36] also introduced a kernel-based suppressed FCM version, using the suppression formula derived from Eq. (29).…”

Section: Time Variant Suppression Rate Based On Current Cluster Protomentioning

confidence: 99%