The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.

中文翻译：

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非凸正则化项约束下基于局部熵拟合的图像分割

CV和LBF模型的能量泛函单一，使得曲线在演化过程中容易进入局部最小值，导致灰度不均匀、边缘不平滑的图像分割不准确。该算法基于非凸正则化项约束下的局部熵拟合，用于处理此类问题。该算法通过拟合全局信息和局部熵来避免分割陷入局部最优，并引入非凸正则化项进行约束以保护边缘平滑。首先，使用全局信息来演化目标分割的近似轮廓曲线。然后，构造具有局部熵信息的局部能量泛函，以避免分割过程陷入局部最小值，并精确分割图像。最后，在能量泛函中使用非凸正则化项来保护图像分割过程中边缘信息的平滑性。实验结果清楚地表明，新算法能够有效抵抗噪声，精确分割灰度不均匀的图像，实现全局最优。