【作者】 宣浩；

【导师】 蒋建国； 詹曙；

【作者基本信息】 合肥工业大学 ， 信号与信息处理， 2010， 硕士

【摘要】 随着经济水平的提高,健康已成为现代社会主题之一。医学影像技术使我们可以非侵入地观察人体内部构造和诊断治疗。而医学图像处理和分析作为信息科学技术和医学的交叉学科,将使医学向数字化、智能化、自动化方向迈进。本文针对椎间盘、椎骨和半月板这三种富于细节变化的核磁共振图像,进行统计形状建模和图像分割处理,具有较强的实用意义。主要工作和创新点如下:(1)针对统计形状建模中的点对应问题,使用基于最小描述长度的最优化方法加以解决,完成模型的自动建立。并提出使用多尺度弧长参数函数,确保粗尺度上具有最优意义的点对应,同时在精尺度上使用最简单的弧长参数函数来确定特征点,完成对感兴趣目标的快速统计形状建模。为后续图像分割或定量分析打下基础。实验对肌肉骨骼核磁共振成像中椎骨、椎间盘以及半月板等具有临床意义的结构建立了统计形状模型,验证了本文方法与手动取点相比具有客观可重复性且更加简洁,与单一尺度下的最小描述长度方法相比时间效率更高。(2)主动形状模型是一种行之有效的图像分割方法,在建立主动形状模型中关键的一点是从形状样本集中获得满足点对应关系的轮廓采样点集合。传统的手动标定这些特征点枯燥,耗时,且带有主观性。本文先使用基于最小描述长度优化的方法自动建立统计形状模型,并以得到的点分布模型为基础,快速建立灰度阶外观模型,将之用于相关医学图像的分割,并与手动建模的分割结果相比较,误差相当或有所降低。更多还原

【Abstract】 Along with the improvement of economics,health is one of the key topics in our modern society.Medical imaging provides a non-intervention overview of inner structures of our bodies and great help in diagnosis and cure.Combining fields like information science and medicine,medical image process and analysis is making another push in the progress of medicine digitally,automatically and intelligently.In this dissertation,research is done on Statistical Shape Modeling and Segmenting in intervertebral disc, vertebra and meniscus MRI.The main points are as follows:(1) we describe a minimum description length based optimization method for automatically building statiscal shape models from training set of example boundaries. A multi-scale parameterization on shapes allows the optimization on landmark correspondence in a coarse scale and a most convenient arc parameterization based landmark correspondence in a fine scale. This achieves a fast and accurate SSM building, which is the foundation on following image segmentation and quantitative analysis. In experiments, SSMs are built with vertebral body, intervertebral disc and meniscus shapes extracted from various MRIs respectively. It is testified that the models built with the proposed scheme is not only more repeatable and concise than model based on manually landmarking, but also more temporally efficient than model purely based on optimization.(2) Active Shape Model (ASM) is an efficient method of image segmenting. One key factor in building models is obtaining correspondent landmarks among the whole shape dataset. Traditional manual landmarking is temporally expensive, subjective,boring and prohibitively extensive in dimension. In this dissertation, a parameterization on shapes allows a Minimum Description Length (MDL) based optimization on landmark correspondence. Base on the point distribution models, we complete gray-level models building, which is the foundation on following image segmentation. The segmentation errors from the proposed method are comparable with or better than those from the manual modeling based segmentation.更多还原