一种基于动力学统计形状模型的超声图像分割方法  

A novel ultrasound image segmentation based on a dynamical statistical shape model

作　　者：倪波[1,2] 何发智[1] 袁志勇[1] 

机构地区：[1]武汉大学计算机学院,武汉430033 [2]湖北理工学院计算机学院,黄石435003

出　　处：《中国科学：信息科学》2015年第11期1449-1465,共17页Scientia Sinica(Informationis)

基　　金：国家自然科学基金(批准号:61472289);国家重点基础研究发展计划(973计划)(批准号:2011CB707904)资助项目

摘　　要：从2D超声图像中分割出病灶区域的轮廓是计算机辅助治疗中制定术前计划的重要步骤.然而,有效的超声图像分割方法依旧是一个挑战性难题,主要原因有两个:(1)超声图像自身固有的低信噪比和灰度分布不均匀造成的病灶区域局部边缘模糊;(2)成像过程中病人呼吸或外部挤压容易造成体内软组织的形变从而引起病灶区域的几何轮廓的复杂性.这些因素导致许多分割方法不容易收敛到真实的病灶区域.为解决以上问题,本文提出了一种基于动力学统计形状模型的分割方法.首先,该方法定义一种时间序列模型用作病灶区域形状的参数化表示模型,并用随机差分方程(stochastic difference equation,SDE)作为轮廓的动力学生成模型;然后利用Fokker-Planck方程(FPE)的性质和对样本集进行统计建模推导出动力学生成模型的系数项和形状先验概率,我们称此统计形状模型(statistical shape model,SSM)为SF-SSM;此外,根据超声图像中病灶区域内外灰度变化的统计信息并结合半径时间序列模型构建了一种径向轮廓特征模型(radial profile feature model,RPFM),该模型为先验形状提供置信区域和似然概率;最后,分割的优化过程被转化为求最大后验概率的过程.为了验证本文方法的性能,采用真实的临床超声图像作为训练和测试集合,并与其他3种典型的分割方法在同一测试集合中进行比较.实验结果显示,在不同质量的超声图像中,本文方法提供了更准确的分割结果.此外,该方法友好的初始化方式提高了交互式分割的效率,从而提高了计算机辅助治疗的效率和效果.Segmenting the lesion region from 2D ultrasound （US） images is an importance step in defining the intra-operative planning of computer-assisted therapy. However, it still remains a challenge owing to two reasons： （1） The signal-to-noise ratio （SNR） of the US images is originally low, and the inhomogeneous distribution of in- tensity causes local fuzzy boundaries. （2） The deformation of soft tissue causes makes the geometry contour of the segmentation area complex. Because of these factors, the classical segmentation methods do not yield the desired results in US images. To solve these problems, a novel segmentation method based on a dynamics statistical shape model is proposed. In this study, first, a radial time series model was defined for use as a parametric shape model of the lesion region, and a stochastic difference equation （SDE） was used as the contour dynamics generating model; then, the coefficients of the dynamics generating model and the shape prior probability were deduced by utilizing the property of Fokker-Planck equation （FPE） and training the samples. This statistical shape model （SSM） was referred to as the SF-SSM. Furthermore, a radial profile feature model （RPFM） was developed based on the statistical features of the intensity distribution from the inside to outside of the lesion region and based on the radial time series model. The RPFM provided a likelihood probability for the prior shape. Finally, through the segmentation optimization process, the maximum posteriori process was determined. In order to verify the performance of our method, we used real clinical ultrasound images as the training and test sets. We compared the proposed model with three well-known segmentation methods for the same test set. The experimental results showed that the proposed method provided more accurate results than the other methods for different quality US images. Furthermore, the friendly initialization of the proposed method improves the efficiency of interactive segmentatio

关 键 词：动力学模型 FOKKER-PLANCK方程 统计形状模型 超声图像分割 计算机辅助治疗 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]