Fast and Efficient Numerical Finite Difference Method for Multiphase Image Segmentation
Author(s) -
Yibao Li,
Sungha Yoon,
Jian Wang,
Jintae Park,
Sangkwon Kim,
Chaeyoung Lee,
Hyun Dong Kim,
Junseok Kim
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/2414209
Subject(s) - initialization , algorithm , segmentation , image (mathematics) , numerical analysis , stability (learning theory) , process (computing) , image segmentation , operator (biology) , property (philosophy) , simple (philosophy) , flow (mathematics) , mathematics , computer science , artificial intelligence , mathematical analysis , geometry , biochemistry , chemistry , philosophy , epistemology , repressor , machine learning , transcription factor , gene , programming language , operating system
We present a simple numerical solution algorithm for a gradient flow for the Modica–Mortola functional and numerically investigate its dynamics. The proposed numerical algorithm involves both the operator splitting and the explicit Euler methods. A time step formula is derived from the stability analysis, and the goodness of fit of transition width is tested. We perform various numerical experiments to investigate the property of the gradient flow equation, to verify the characteristics of our method in the image segmentation application, and to analyze the effect of parameters. In particular, we propose an initialization process based on target objects. Furthermore, we conduct comparison tests in order to check the performance of our proposed method.
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