Due to uneven illumination or limitations of imaging devices, intensity inhomogeneities are more or less present in images obtained by different imaging modes. This ubiquitous intensity inhomogeneity makes image segmentation more difficult. This paper proposes a new bias field model (KPBFE) based on pre-fitting bias field estimation to deal with intensity inhomogeneity in the image segmentation. A new function for computing bias field $b$ is proposed with K-means++ clustering algorithm. The computation method of clustering center points takes into account the average value of the grayscale within the contour of the bias field estimation and outside the contour. Meanwhile, we use a variational level set function with arctan function and a new adaptive function $\tau$ to limit the magnitude of the data driver term. Since the computation of bias field estimation is completed before the iteration and there is no convolution operation in the process, the computing speed of the proposed model is greatly increased. Experiments results show that our model can effectively segment the images with intensity inhomogeneity. Compared with some classical models, our method also has faster computation speed, higher segmentation accuracy and better initial robustness.

由于照明不均匀或成像设备的限制，不同成像模式获得的图像或多或少存在强度不均匀性。这种无处不在的强度不均匀性使得图像分割更加困难。本文提出了一种基于预拟合偏置场估计的新偏置场模型（KPBFE）来处理图像分割中的强度不均匀性。计算偏置场的新函数$乙$提出了K-means++聚类算法。聚类中心点的计算方法考虑了偏置场估计轮廓内和轮廓外的灰度平均值。同时，我们使用带有反正切函数的变分水平集函数和新的自适应函数$\tau$限制数据驱动项的大小。由于偏置场估计的计算是在迭代之前完成的，并且过程中没有卷积操作，所以所提模型的计算速度大大提高。实验结果表明，我们的模型可以有效地分割强度不均匀的图像。与一些经典模型相比，我们的方法还具有更快的计算速度、更高的分割精度和更好的初始鲁棒性。