改进的局部扩展拟合图像分割方法

摘要 局部扩展拟合(RSF)模型可以有效解决灰度不均匀的图像分割问题，但传统的局部拓展拟合模型只考虑了图像上一点在其局部拓展区域内与零水平集相交的情况，易导致过分割现象.为此， 提出了一种改进的局部拓展拟合方法，即在RSF模型的基础上考虑图像上每一点在其扩展邻域内与零水平集不相交的情况，并重新定义了此情况下灰度能量的取值，使该点的内部灰度能量与外部灰度能量严格相等.实验结果表明所提方法有效解决了由初始化问题导致的过分割现象.
Abstract：The region-scalable fitting(RSF) model can effectively solve the problem of intensity inhomogeneities， but the traditional RSF model only considers the case where the point on the image intersects with the zero level set in its local extension area， which easily leads to over-segmentation. To solve the problem， an improved region-scalable fitting method was proposed， i.e.， the case where every point in the image does not intersect with the zero level set in its extended neighborhood was considered based on the RSF model， and the value of intensity energy in this case was redefined to make the inner intensity energy and the outer intensity energy strictly equal. Experiment results showed the proposed method effectively solves the over-segmentation caused by the initialization problem.