A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization
A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization
复制标题
一种基于粒子群优化的统计脑血管分割算法
DOI：
10.1016/j.neucom.2014.07.006
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发表时间：
2015-01-19
期刊：
影响因子：
6
通讯作者：
Jin, Jesse S.
中科院分区：
文献类型：
--
作者：
Wen, Lei;Wang, Xingce;Jin, Jesse S.
我们提出了一种基于自动统计强度的方法，从飞行时间（TOF）磁共振血管造影（MRA）数据中提取三维脑血管结构。我们使用有限混合模型（FMM）拟合脑图像序列的强度直方图，其中脑血管结构采用高斯分布函数建模，其他低强度组织采用高斯和瑞利分布函数建模。为了估计FMM的参数，提出了一种改进的粒子群优化算法（PSO），该算法在加速更新粒子群优化算法的公式以保证其收敛性方面增加了干扰项。我们还利用粒子邻域的环状拓扑结构来提高算法的性能。34个测试数据的计算结果表明，该方法对小血管的分割效果较好。(C) 2014年作者。这是一篇基于CC by - nc - sa许可的开放获取文章。
We present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-SA license.