A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation

In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Jg. 41; H. 5; S. 1263 - 1274
Hauptverfasser: Long Chen, Chen, C. L. P., Mingzhu Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2011
Schlagworte:
Algorithm design and analysis
Algorithms
Animals
Brain - anatomy & histology
Clustering algorithms
Composite kernel
Computer Simulation
Cybernetics
Dogs
Fuzzy
fuzzy C-means (FCM)
Fuzzy Logic
Fuzzy set theory
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Kernel
kernel function
Kernels
Magnetic Resonance Imaging
multiple kernel
Pixels
Prototypes
Segmentation
Transforms
ISSN:1083-4419, 1941-0492, 1941-0492
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The proposed MKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in image-segmentation problems. That is, different pixel information represented by different kernels is combined in the kernel space to produce a new kernel. It is shown that two successful enhanced KFCM-based image-segmentation algorithms are special cases of MKFCM. Several new segmentation algorithms are also derived from the proposed MKFCM framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of MKFCM-based approaches.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2011.2124455