生物医学图像分析可以辅助医生诊断疾病,然而,图像中常含有噪声以及灰度不均匀现象,使得传统的图像分割方法不能得到满意的结果。针对这些问题,构造一种基于图像区域信息的偏移场恢复耦合模型,使得模型可以在分割的同时恢复出图像偏移场。为了得到全局最优解并提高算法效率,将该模型改进成1范数下的凸函数,并使用基于Split-Bregman方法对该耦合模型进行快速求解。实验结果表明,本文方法可以降低噪声和灰度不均匀的影响,得到较准确的分割结果和偏移场信息,而且大大地降低了计算复杂度。

Medical image analysis is helpful for doctors to diagnose diseases. However, the images usually have noise and intensity inhomogeneities, which makes it hard to obtain satisfactory results using the traditional image segmentation methods. To solve these problems, we propose a coupled model based on local image information, which can segment images while restoring the bias field. In order to obtain global optimal results accurately and quickly, we improved the coupled model to be a convex function and solved it based on the Split-Bregman method. The experimental results show that our method can reduce the effect of the noise and intensity inhomogeneities, and obtain more accurate segmentation results while estimating the bias field efficiently.