Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation

Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. In this study, a modified FCM algorithm is presented by utilising local contextual information and structure information. The authors first establish a novel similarity measure model based on image patches and local...

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Vydáno v:IET image processing Ročník 8; číslo 3; s. 150 - 161
Hlavní autoři: Zaixin, Zhao, Lizhi, Cheng, Guangquan, Cheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Stevenage The Institution of Engineering and Technology 01.03.2014
Institution of Engineering and Technology
The Institution of Engineering & Technology
Témata:
Applied sciences
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Euclidean distance replacement
Exact sciences and technology
fuzzy set theory
Image processing
image segmentation
Information, signal and communications theory
local contextual information
Medical management aid. Diagnosis aid
Medical sciences
modified FCM algorithm
neighbourhood weighted distance
neighbourhood weighted fuzzy c‐means clustering algorithm
objective function
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
structure information
Telecommunications and information theory
neighbourhood weighted fuzzy c-means clustering algorithm
image segmentation
fuzzy set theory
modified FCM algorithm
structure information
neighbourhood weighted distance
local contextual information
Euclidean distance replacement
objective function
Image segmentation
Automatic classification
Image processing
Similarity
Medical imagery
Artefact
Objective function
Information structure
Algorithm
Signal classification
Nuclear magnetic resonance imaging
ISSN:1751-9659, 1751-9667
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Popis
Shrnutí:Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. In this study, a modified FCM algorithm is presented by utilising local contextual information and structure information. The authors first establish a novel similarity measure model based on image patches and local statistics, and then define the neighbourhood-weighted distance to replace the Euclidean distance in the objective function of FCM. Validation studies are performed on synthetic and real-world images with different noises, as well as magnetic resonance brain images. Experimental results show that the proposed method is very robust to noise and other image artefacts.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2011.0128