Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C -Means Clustering

An adaptively regularized kernel-based fuzzy C -means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and d...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational and mathematical methods in medicine Jg. 2015; H. 2015; S. 1 - 12
Hauptverfasser: Li, Guanglin, Wu, Jianhuang, Jia, Fucang, Wang, Changmiao, Elazab, Ahmed, Hu, Qingmao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
Schlagworte:
Algorithms
Brain - pathology
Brain Neoplasms - pathology
Cluster Analysis
Computational Biology
Fuzzy Logic
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - statistics & numerical data
Neuroimaging - methods
Neuroimaging - statistics & numerical data
ISSN:1748-670X, 1748-6718, 1748-6718
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:An adaptively regularized kernel-based fuzzy C -means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Academic Editor: Jesús Picó
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2015/485495