基于正交多项式密度函数的CT图像分割方法

摘要

The complex medical images are not always described the parametric method with prior probability,which leads to the difference between the actual physical model and the basic hypothesis of the model,namely,the “model mismatc”.The method of CT image segmentation based on the multi-modal function optimization was proposed.A density model of the nonparametric orthogonal polynomials for image data was proposed,and the novel particle swarm optimization method was used to solve the multi-modal function optimization problem.On the basis of the heuristic optimization search,the novel method was successfully applied in multi-model function optimization. Some experiments about artificial data and real CT images were tested.These results show the validity of the algorithm in image segmentation.%通过构建面向CT图像数据的非参数正交多项式密度模型，解决了带有先验性质的参数化方法不能对复杂医学图像做出合适地描述，从而导致实际物理模型与假设模型间存在较大差异的问题，即模型不匹配的问题。在形成的多峰函数基础上采用改进的粒子群优化算法，依据启发式优化搜索策略实现多峰优化，先寻找多个局部最优解，再以局部最优解为聚类中心实现图像分割。通过模拟数据和真实CT图像数据的仿真，证明了基于正交多项式密度函数图像分割算法的有效性。