Abstract
Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.
Highlights
Image segmentation has always been one of the difficulties in image processing and computer vision
We present the details of the proposed weighted region-based active contour model (ACM), which is based on the techniques of local statistical theory, curve evolution and the level set method
Based on the weighted pressure force (WPF), we propose an enhanced distance regularized LSM (EDRLSM), which uses a polynomial function instead of a trigonometric function to further improve the speed of the algorithm
Summary
Image segmentation has always been one of the difficulties in image processing and computer vision. Liu et al utilized local regional fitting information to propose an improved level set method, which can differentiate the noise and boundary points of the image to be segmented. In this model, two innovations are proposed: first, a controllable velocity coefficient was proposed to accelerate the curve convergence speed, and second, a new edge stop function was constructed to enhance the performance of the segmentation model. The LRFI model demonstrates good performance in segmenting noisy images
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