Intensity inhomogeneity, which is also called bias field, is ubiquitous in digital images. The causes of intensity inhomogeneity are complex and include uneven illumination and defects of imaging equipment. For images with local intensity inhomogeneity, an array of existing segmentation algorithms has poor performance on efficiency, accuracy, or initial robustness. To tackle this problem, an active contour model based on local prefitting bias estimation is proposed. The bias field is approximated through a new function based on a mean filtering algorithm, which can credibly represent the distribution of bias field of an input image. Then, the bias field is incorporated into the optimized energy functional based on the level set method to implement the segmentation process. Specifically, the bias field is computed before iterations and the mean filtering algorithm is much faster than traditional clustering algorithm, so the efficiency is greatly raised. Moreover, a new regularization function is formulated to improve the robustness of the initial contour and noise. Comparing with some traditional models, the proposed model achieves better results on some challenging images.

中文翻译：

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具有局部预拟合偏差估计的主动轮廓模型用于快速图像分割

强度不均匀性（也称为偏置场）在数字图像中无处不在。强度不均匀的原因很复杂，包括不均匀的照明和成像设备的缺陷。对于具有局部强度不均匀性的图像，一系列现有的分割算法在效率，准确性或初始鲁棒性方面性能较差。针对这一问题，提出了一种基于局部预拟合偏差估计的主动轮廓模型。通过基于均值滤波算法的新函数来近似偏置场，该函数可以可靠地表示输入图像的偏置场的分布。然后，基于水平集方法将偏置场合并到优化的能量函数中，以实现分段过程。具体来说，偏差场是在迭代之前计算的，并且均值滤波算法比传统的聚类算法要快得多，因此效率大大提高了。此外，制定了新的正则化函数以提高初始轮廓和噪声的鲁棒性。与一些传统模型相比，所提出的模型在一些具有挑战性的图像上取得了更好的结果。