Open AccessHybrid fitting energy‐based fast level set model for image segmentation solving by algebraic multigrid and sparse field method
Author(s) -
Wang Dengwei
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0786
Subject(s) - level set (data structures) , computer science , image segmentation , multigrid method , field (mathematics) , algebraic number , set (abstract data type) , energy (signal processing) , image (mathematics) , artificial intelligence , segmentation , sparse approximation , algorithm , mathematics , statistics , partial differential equation , mathematical analysis , pure mathematics , programming language
A novel hybrid fitting energy‐based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalises the deviation of the level set function from a signed distance function is also included in the authors’ method, so the complex and costly reinitialisation procedure is completely eliminated. Their model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, two efficient numerical schemes are used to ensure the sufficient efficiency of the evolution process, one is the algebraic multigrid, which is used for breaking the restrictions on time step; the other is the sparse field method, which is introduced for fast local computation, compared with the traditional schemes, these two strategies can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.