Fuzzy c-means clustering with non local spatial information for noisy image segmentation

DOI10.1007/S11704-010-0393-8zbMath1267.68198OpenAlexW1967584880MaRDI QIDQ352035

Hanqiang Liu, Feng Zhao, Li-Cheng Jiao

Publication date: 4 July 2013

Published in: Frontiers of Computer Science in China (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11704-010-0393-8

zbMATH Keywords

image segmentationfuzzy clustering algorithmmagnetic resonance (MR) imagenon local spatial information

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55)

Related Items (2)

Active contour model based on improved fuzzy c-means algorithm and adaptive functions ⋮ Stability-integrated fuzzy \(C\) means segmentation for spatial incorporated automation of number of clusters

Cites Work

• Fast and robust fuzzy \(c\)-means clustering algorithms incorporating local information for image segmentation
• A clustering fuzzy approach for image segmentation
• Geometric Level Set Methods in Imaging, Vision, and Graphics
• A Review of Image Denoising Algorithms, with a New One
• Cluster Validity with Fuzzy Sets
• Unnamed Item

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