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Image Segmentation Using Binary Level Set Method Based on Region-based GAC Model

机译:基于区域GAC模型的二进制水平集图像分割

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A new Region-based GAC (geodesic active contour) model was presented, which is the improvement of traditional GAC model. A new region-based signed pressure forces function was presented, which takes the place of the edge stopping function, and can efficiently solve the problem of segmentation of objects with weak edges or without edges. The model is implemented by level set method with a binary level set function to reduce the expensive computational cost of re-initialization of the traditional level set function. The proposed algorithm has been applied to images of different modalities with promising results, which are better than that of traditional GAC model and C-V model.
机译:提出了一种基于区域的GAC(大地活动轮廓线)模型,它是对传统GAC模型的改进。提出了一种新的基于区域的有符号压力函数,该函数取代了边缘停止功能,可以有效解决弱边缘或无边缘对象的分割问题。该模型通过具有二进制级别集函数的级别集方法来实现,以减少传统级别集函数重新初始化的昂贵计算成本。所提出的算法已应用于不同模态的图像,效果令人满意,优于传统的GAC模型和C-V模型。

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