小邻域统计信息核磁共振医学图像分割模型

Magnetic resonance medical images segmentation based on a local statistical information model

目的提出局部统计信息测地线活动轮廓图像分割方法。方法该方法采用高斯分布拟合图像局部灰度统计特征信息,构造了方向性驱动项。在此基础上,建立了局部统计信息测地线能量泛函。通过极小化该泛函,来驱动演化曲线有序地向目标边界逼近,最后,整个分割过程采用二值水平集方法实现。结果本文方法降低了灰度不均匀信息影响,达到提取感兴趣区域轮廓目的,提高算法效率和稳定性。结论实验结果表明,该方法可以快速准确地分割医学感兴趣目标边界。

Objective In this paper, we propose a local statistical geodesic active contour （GAC） image segmentation method. Method Local intensity statistical information according with the Gaussian distribution is assumed. A directional driving item is established in order to reduce the effect of intensity inhomogeneity information. Second, a local statistical geodesic active contour energy function based on this hypothesis is established. Result By minimizing the proposed energy functional, it can orderly guide the movement of evolution curve to object boundaries for achieving regions of interest （ROI） segmentation. Finally, the method is implemented by a binary level set function in order to improve the algorithm＇s efficiency and stability. Conclusion Experiment results with medical images show that the algorithm can segment ROI of medical images fast and accurate.