【作者】 王超；

【导师】 鲍虎军； 刘华锋；

【作者基本信息】 浙江大学 ， 计算机应用， 2006， 硕士

【摘要】 医学图像分割,作为图像学检查的第一步,具有重要的临床意义。它是正常组织和病变组织的三维重建,定量分析等后继操作的关键,是结构分析、运动分析、三维可视化、图像引导手术、肿瘤放射治疗、治疗评估的基础,也是临床医学应用的瓶颈。分割的准确性对医生判断疾病的真实情况并做出正确诊断计划至关重要。 近年来,可变形模型,尤其是其中的Snakes模型,在医学图像分割、配准、运动分析等各方面得到了广泛的应用。然而这些方法仅从数学的角度建立模型,以图像为外部数据进行驱动,忽视了医学成像物体本身的物理特性这个重要信息。建立基于物理的可变形模型,考虑弹性体材料的弹性模量和泊松比等基本特性,对于医学图像分割具有及其重要的意义。 在数值方法上,本文分别使用了有限元和无网格方法。有限元方法的基本思想是将一个连续的求解区域离散为一组有限个单元并按一定方式相互联结在一起的集合体,在单元内假设一个近似函数来分片地表示求解域上待求的函数场。无网格方法用一组离散无序的点来表示求解域,而无需预先知道这些点之间的联结关系。通过寻找领域内的点来近似未知点的场函数值。无网格方法由于其摆脱了单元之间的束缚,强调整体求解的误差最小,因而具有比有限元方法更高的分割精度。且当待分割物体发生较大形变时,无需重新进行单元剖分。实验证明,有限差分,有限元,无网格分别具有递增的分割精度。更多还原

【Abstract】 Medical image segmentation, as the first step of medical image analysis, is fundamental to surgical planning and simulation. It is the key to the 3D construction of normal or abnormal structure and quantitive analysis such as structure analysis and motion analysis. It also forms the basis of 3D visualization, image guided surgery, motion estimation, image registration, tissue state assessment, etc. Accurate segmentation is important for the doctor to reach proper clinical diagnoses.Deformable model, especially Snake, a promising and vigorously researched computer-assisted medical image analysis technique is widely used in segmenting, matching, and motion tracking. Deformable modeling only takes the perspective of mathematics, which omits the important information from the characteristic of the material itself. Physics-based deformable model is introduced in this paper which combine geometry, physics, and approximation theory together by taking the characteristic of the material into consideration, such as Young’s modulus and Poisson’s ratio. Deformation process is governed by basic laws of non-rigid motion.As for the numerical method, we use FEM methods and meshfree methods respectively. The base idea of FEM is to discretize the domain of interests into elements which provide spatial relationships between the sampling nodes. The main computational power of the FEM results from the fundamental idea of replacing a continuous function defined over the entire domain by piecewise approximations over a set of finite number of geometrically simple domains. Meanwhile, the recently developed meshfree methods represent the interested spatial domains with only a set of nodal points but without any mesh constraints which eliminate at least part of the mesh structure by constructing the approximation of the field function and the discrete system equations entirely in terms of the nodes. They can more naturally handle very large deformation and discontinuity. Experiments on synthetic and real images show their robustness and accuracy in medical image segmentation.更多还原