Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation

• URL: http://arxiv.org/abs/2310.08861v2
• Date: Wed, 26 Jun 2024 08:22:31 GMT
• Title: Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation
• Abstract summary: We propose a high-order level set variational segmentation method integrated with molecular beam epitaxy (MBE) equation regularization. This method uses the crystal growth in the MBE process to limit the evolution of the level set function. It can generate smooth segmentation curves, retain fine segmentation targets and obtain robust segmentation results of small objects.
• Score: 0.41942958779358674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational level set method has become a powerful tool in image segmentation due to its ability to handle complex topological changes and maintain continuity and smoothness in the process of evolution. However its evolution process can be unstable, which results in over flatted or over sharpened contours and segmentation failure. To improve the accuracy and stability of evolution, we propose a high-order level set variational segmentation method integrated with molecular beam epitaxy (MBE) equation regularization. This method uses the crystal growth in the MBE process to limit the evolution of the level set function, and thus can avoid the re-initialization in the evolution process and regulate the smoothness of the segmented curve. It also works for noisy images with intensity inhomogeneity, which is a challenge in image segmentation. To solve the variational model, we derive the gradient flow and design scalar auxiliary variable (SAV) scheme coupled with fast Fourier transform (FFT), which can significantly improve the computational efficiency compared with the traditional semi-implicit and semi-explicit scheme. Numerical experiments show that the proposed method can generate smooth segmentation curves, retain fine segmentation targets and obtain robust segmentation results of small objects. Compared to existing level set methods, this model is state-of-the-art in both accuracy and efficiency.

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