An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper The energy functional is formulated through incorporating the adaptive edge indicator function and level set formulation driven by bias field correction. The adaptive edge indicator function, which is formulated based on image gradient information, is utilized to detect object boundaries and accelerate the segmentation in the homogeneous region. The level set formulation is constructed based on an improved criterion function, in which bias field information is considered. Specifically, the bias field distribution is approximated through the local mean gray value algorithm as a prior. Moreover, a new regularized function is proposed so as to maintain the stability of curve evolution. The segmentation process is implemented by the optimized energy function and the novel regularized term. Compared to previous active contour models, the modified active contour method can yield more precise, stable, and efficient segmentation results on some challenging images.

现有的一系列主动轮廓模型容易存在抗噪能力差、初始化敏感、收敛慢等不足。为了解决这些问题，本文提出了一种基于偏置校正的鲁棒混合主动轮廓方法。能量泛函是通过结合自适应边缘指示函数和偏置场校正驱动的水平集公式来制定的。基于图像梯度信息制定的自适应边缘指示函数用于检测对象边界并加速均匀区域的分割。水平集公式是基于改进的标准函数构建的，其中考虑了偏置场信息。具体来说，偏置场分布是通过局部平均灰度值算法作为先验近似的。此外，提出了一个新的正则化函数，以保持曲线演化的稳定性。分割过程由优化的能量函数和新的正则项实现。与以前的活动轮廓模型相比，改进的活动轮廓方法可以在一些具有挑战性的图像上产生更精确、稳定和高效的分割结果。