局部GAC模型在医学图像分割中的应用

针对基于区域测地线活动轮廓(GAC)模型很难准确分割灰度不均匀图像的问题,提出基于局部信息的GAC模型。该方法首先将图像区域进行局部化,来克服灰度不均匀对分割结果的影响,然后构造局部符号压力函数(ISPF)指导轮廓线在目标外部(或内部)收缩(或扩张)来完成分割。为了提高算法效率和稳定性,用二值水平集方法实现整个分割过程,避免了传统水平集数值不稳定性。实验结果表明,本文方法可以快速有效地分割灰度不均匀的医学图像。

Application of local GAC model for medical image segmentation

The geodesic active contour (GAC) model based on regions is not applicable to images with intensity inhomogeneity. In this paper,we propose a new model of the GAC based on local regions. Information of the local mean is used to overcome the intensity inhomogeneity effect of the segmentation result. A local signed pressure force function is constructed so that the contour shrinks when outside of the object, or expands when inside of the object. In order to improve the algorithm's effectively and steadily, the model is implemented by a binary level set function. Experimental results with medical images show that the new model can get the better results in a more efficient way.