Brain MR image segmentation based on an adaptive combination of global and local fuzzy energy

DOI10.1155/2013/316546zbMath1296.94025OpenAlexW1988412889WikidataQ59025961 ScholiaQ59025961MaRDI QIDQ460440

Tao Lei, Yangyu Fan, Yan Feng, Wenchao Cui, Yi Wang

Publication date: 13 October 2014

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/316546

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Fuzzy sets and logic (in connection with information, communication, or circuits theory) (94D05)

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Cites Work

• Convex image segmentation model based on local and global intensity fitting energy and split Bregman method
• Global and local fuzzy energy-based active contours for image segmentation
• Split Bregman method for minimization of improved active contour model combining local and global information dynamically
• Geodesic active regions and level set methods for supervised texture segmentation
• Local- and global-statistics-based active contour model for image segmentation
• Active contours driven by local Gaussian distribution fitting energy
• An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities
• Minimization of Region-Scalable Fitting Energy for Image Segmentation
• A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

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