基于偏移场的双水平集医学图像分割算法

Segmentation of Medical Images Based on Bias and Double Level Set Algorithm

针对医学图像中通常伴有灰度不均、背景复杂,无法被传统水平集有效分割的特点,提出了基于偏移场的双水平集算法。为了去除医学图像中灰度不均对分割效果的影响,算法中引入偏移场拟合项,改进双水平集模型,再由改进后的双水平集算法分割医学图像中的多目标区域。实验结果表明,所提算法能有效地解决灰度不均与背景复杂的问题,将伴有灰度不均的多目标医学图像完全分割出来,获得预期的分割效果。

This paper proposes a novel bias and double level set algorithm for medical image,which has a large amount of intensity inhomogeneities and complicated background,and cannot be separated completely by traditional level set. First of all,In order to deal with the effect of intensity inhomogeneities on the medical image,the algorithm introduces a bias fitting term into the improved double level set model and optimizes the coarse-scale segmentation result. Experimental result shows that the algorithm can reduce the problems of intensity inhomogeneities and complicated background,separate medical image including intensity inhomogeneities and multiple objects completely,and obtain the expected effect of segmentation.