Level set segmentation of medical images based on local region statistics and maximum a posteriori probability

DOI10.1155/2013/570635zbMath1307.92166OpenAlexW1997630379WikidataQ37329662 ScholiaQ37329662MaRDI QIDQ2262237

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

Publication date: 16 March 2015

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/570635

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55)

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New region-scalable discriminant and fitting energy functional for driving geometric active contours in medical image segmentation

Cites Work

• Geometric applications of the split Bregman method: segmentation and surface reconstruction
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• Geodesic active contours
• A multiphase level set framework for image segmentation using the Mumford and Shah model
• Using prior shapes in geometric active contours in a variational framework
• Active contours driven by local Gaussian distribution fitting energy
• Active contours without edges
• Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification
• 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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