基于邻域信息的B样条密度模型的医学图像分割研究

针对有参混合模型的聚类算法需要假设模型为某种已知的参数模型而存在模型不匹配及应用于图像分割时对噪声比较敏感的问题,提出了一种基于空间邻域信息的B样条密度模型的图像分割方法。首先,通过构建基于规范化的B样条密度函数的非参数混合模型,定义空间信息函数,使得分割模型具有空间邻域信息;其次,利用非参数B样条期望最大(NNBEM)算法估计密度模型的未知参数;最后根据贝叶斯准则实现图像的分割。该图像分割方法不需要假设图像符合某种模型,就可以克服实际数据分布与假设图像模型不一致的问题。此方法有效克服了"模型失配"问题,而且有力抑制了噪声点,同时很好地保留了边界的特性。分别对模拟图像进行仿真,验证了基于空间邻域信息的B样条密度模型的分割方法的有效性。

Medical Image Segmentation Based on Non-parametric B-spline Density Model with Spatial Information

Because finite mixture model for parameters estimation method partially depends on the prior assumption and is sensitive to noise in image segmentation,a non-parametric medical image B-spline density model with spatial information segmentation method was proposed in this paper.First,the image non-parametric B-spline density model was designed,and spatial information function was defined in order to make the model with spatial neighborhood information.Secondly,non-parametric B-spline expectation maximum(NNBEM) algorithm was used to estimate the unknown parameter of the density model.Finally,image was clustered according to the Bayesian criterion.This method effectively overcome the model mismatch problem,which is not only effective to deal with noisy,but also reserve edge property well.The experimental results about the simulation image segmentation show the effeciveness of this method.