Based on the local correntropy-based K -means clustering active contour model, this paper proposes a fast two-stage segmentation method for intensity inhomogeneous images. Under our framework, the segmentation process is split into two stages. In the first stage, we preliminary segment the down-sampled images by the proposed relaxed anisotropic–isotropic local correntropy-based K -means clustering (AILCK) model, which can obtain a coarse segmentation result quickly. Subsequently, in the second stage, we further segment original images by an improved AILCK model, where we use the up-sampled coarse contour obtained by the first stage as the initialization. Following it, to obtain the global minima of energy functions fast, we incorporate a weighted difference of anisotropic and isotropic total variations into relaxed formulation of the two-stage active contour models. And then, we minimize them utilizing the difference-of-convex algorithm and the primal–dual hybrid gradient method. The experimental results on synthetic and real-world images demonstrate that the proposed method can achieve accurate segmentation results for intensity inhomogeneous images in a fast way.

中文翻译：

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强度不均匀图像的快速两阶段分割模型

本文基于基于局部相关熵的K均值聚类活动轮廓模型，提出了一种强度不均匀图像的快速两阶段分割方法。在我们的框架下，分割过程分为两个阶段。在第一阶段，我们通过提出的基于松弛各向异性-各向同性局部相关熵的 K 均值聚类（AILCK）模型对下采样图像进行初步分割，该模型可以快速获得粗分割结果。随后，在第二阶段，我们通过改进的 AILCK 模型进一步分割原始图像，其中我们使用第一阶段获得的上采样粗轮廓作为初始化。接下来，为了快速获得能量函数的全局最小值，我们将各向异性和各向同性总变化的加权差异合并到两阶段活动轮廓模型的松弛公式中。然后，我们利用凸差算法和原始-对偶混合梯度法将它们最小化。在合成图像和真实世界图像上的实验结果表明，所提出的方法可以快速实现强度不均匀图像的准确分割结果。