【作者】 赵恒博；

【导师】 刘利雄；

【作者基本信息】 北京理工大学 ， 计算机科学与技术， 2011， 硕士

【摘要】 心脏核磁共振成像(magnetic resonance imaging, MRI)能够提供高分辨率、高品质的医学图像,是当前医学图像分析领域的研究热点之一。借助于心脏核磁共振图像,评定左心室心肌功能,在临床诊断中具有重要意义。对心脏解剖结构的准确描述和功能的定量分析是以准确分割心肌的内外边缘为基础的,尤其是心脏左心室内、外膜的分割。主动轮廓模型对心脏核磁共振图像的分割有良好的效果从而在医学图像处理中得到广泛应用,本文研究重点正是基于Snake模型的心脏核磁共振图像分割,对外力模型的改善、内外膜分割等关键问题进行了分析和研究。受图像质量的影响,对心脏核磁共振图像左心室内、外膜分割在有些情况下得不到很好的分割结果。本文根据目标的先验知识,引入形状约束,从而有效克服了由于图像灰度不均、乳突肌等引起的局部极小对心脏MRI的影响,使得Snake轮廓可以准确地分割心脏左心室。本文提出了一种基于广义法向有偏梯度矢量流(GNBGVF)模型的心脏核磁共振图像左心室内、外膜分割方法。首先提出了主动轮廓模型的广义法向有偏梯度矢量流外力模型,作为对梯度矢量流的改进,该外力模型同时保持了切线方向和法线方向有偏的扩散。其次,根据左心室近似为圆形的形状特点,引入了圆形能量约束,有利于克服心脏核磁共振图像分割难点。为了更加有效的消除噪声对图像分割的影响,本文提出了一种新的外力模型:基于扩展邻域和噪声平滑的广义梯度矢量流(ENGGVF)模型,并采用这一外力模型来分割左心室内、外膜。该模型在梯度矢量流的基础上,引入了权重因子和扩展邻域卷积运算,还在拉普拉斯算子模板中加入噪声平滑模板。同时考虑到心脏在舒张期内外膜更接近于椭圆的特点,在Snake模型中添增椭圆形状能量约束。实验表明,该方法能够快速高效准确地分割左心室内、外膜。更多还原

【Abstract】 Cardiac magnetic resonance imaging (MRI) can provide high- resolution and high-quality medical images, which has been a hot topic in the community of medical images analysis. Cardiac magnetic resonance images can help to estimate the myocardium function of ventricles for clinical diagnosis. In order to make thorough use of the anatomical and functional information derived from images, the epicardium and endocardium of the left ventricle should be extracted. Since active contour (Snake) model has a great effect on segmentation of cardiac MR images, it has been widely used in medical image processing. This dissertation focuses on the segmentation of cardiac MR images based on snake model, which contains improved external models,segmentation of the left ventricle, etc.To acquire satisfactory segmentation of the epicardium and endocardium of the left ventricle cardiac MR images, an energy constraint about shape based on a priori knowledge of the target is adopted. The shape constraint can conquer the unexpected local minimum stemming form image inhomogeneity and papillary muscle. With this constraint, the Snake contour is reactivated to locate the left ventricle accurately.A novel method based on generalized normally biased gradient vector flow (GNBGVF) snake model is proposed to segment the left ventricle cardiac MR images. It first proposes an external force for active contours, which is called as generalized normally biased GVF (GNBGVF). As an improvement on gradient vector flow, the GNBGVF external force keeps the diffusion along the tangential direction of the isophotes and biases that along the normal direction simultaneously. Considering that the left ventricle is roughly a circle, a shape constraint based on circle is adopted, which conquers the difficulties in the process of segmenting cardiac MR images.To smooth noise effectively, a novel external force called extended-neighborhood and noise-smoothing generalized gradient vector flow (ENGGVF) is proposed for segmentation of the left ventricle from cardiac MR images. The external force incorporates weighting functions and convolution operation with extended neighborhood and modifies the Laplacian operator mask by adding the noise-smoothing mask. Based on ENGGVF, the shape of the left ventricle is taken into account and an elliptic shape-based energy constraint for snake model is adopted. The proposed strategy is validated on a large amount of cardiac magnetic resonance images.更多还原