An active contour model to segment the images is proposed by combining local binary fitting (LBF) energy function and modified Laplacian of Gaussian (MLoG) energy function. A MLoG energy function based on a new boundary indicator function or edge stop function (ESF) is introduced to smoothen the homogeneous regions and enhance the edge information of objects. Also, MLoG energy term with LBF energy term is incorporated to drive the initial contour towards the object boundary. Finally, the penalty term is replaced with a new optimized potential function, which can improve the corresponding speed function. By adding the optimized area energy term, contour position is accelerated towards the object boundary. Further, the addition of MLoG based on new ESF, makes the proposed model insensitive to the initial contour. Experiments are performed on various real images, MS-COCO 2014 train data set images and Segmentation Evaluation Database images shared in Weizmann Institute of Science website. The proposed model provides better segmentation results compared to the other state of the art models in terms of segmentation accuracy, F -score and CPU execution time. Further, experimental results also prove the robustness of the proposed model in terms of contour initialization, intensity inhomogeneity and noise.

中文翻译：

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主动轮廓由修改的LoG能量项和优化的惩罚项驱动，以进行图像分割

通过结合局部二进制拟合（LBF）能量函数和改进的高斯拉普拉斯算子（MLoG）能量函数，提出了一种主动轮廓模型对图像进行分割。引入了基于新边界指示符函数或边缘停止函数（ESF）的MLoG能量函数，以平滑均质区域并增强对象的边缘信息。而且，结合了MLoG能量项和LBF能量项，以将初始轮廓驱动到对象边界。最后，将惩罚项替换为新的优化的势函数，这可以改善相应的速度函数。通过添加优化的面积能量项，轮廓位置朝着对象边界加速。此外，基于新ESF的MLoG的添加使所提出的模型对初始轮廓不敏感。实验是在Weizmann Institute of Science网站上共享的各种真实图像，MS-COCO 2014训练数据集图像和分段评估数据库图像上进行的。与其他现有技术模型相比，该模型在分割精度方面提供了更好的分割结果，F -分数和CPU执行时间。此外，实验结果还证明了该模型在轮廓初始化，强度不均匀性和噪声方面的鲁棒性。