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医学图像分割算法研究及其在骨分割中的应用

The Research of Medical Image Segmentation Methods and Its Application of Bone Segmentation

【作者】 黄琴波

【导师】 杨兵;

【作者基本信息】 西安电子科技大学 , 生物医学工程, 2011, 硕士

【摘要】 医学图像分割是医学图像处理到分析过程的关键步骤,在临床医学中发挥着越来越重要的作用。其中,骨分割是医学图像分割在临床医学的主要应用,可用于体积测量、骨骼损伤治疗和修复计划的制定、三维重建等。准确的分割结果可为医学研究和临床诊断提供可靠的数据,以提高医生诊断的准确性。本文首先分析了医学图像分割技术的发展现状,主要讨论三大类常用的分割算法,包括:基于变形模型的分割方法、基于区域的分割方法和基于统计学的分割方法以及它们的优缺点。体数据分割是基于医学图像分割技术的新方法,其基本思想是:将序列图像构成的体数据看作一个整体进行处理,提高分割的效率和准确率,该方法对三维体视化有着关键作用。针对CT图像骨骼灰度值变化范围大、存在噪声和弱边缘效应等特点给骨分割带来了很大的困难,本文重点对骨分割算法作进一步的研究。通过算法比较,本文选择分水岭算法对脑部CT图像进行骨分割。从模拟“浸没”过程和集水盆合并两方面进行算法改进以克服过分割现象,从而提高图像的分割质量。对血管造影断层构成的三维体数据,本文提出了一种基于直方图熵的体数据分类方法,通过设置阻光度传递函数的分段点完成去骨目的。

【Abstract】 Medical image segmentation is the key step from medical image process to analysis. As the main application in clinical medicine, bone segmentation can be used for measurement of tissue volume, make bone treatment and rehabilitation programs and reconstruct 3D image. Accurate results of segmentation can provide reliable information for medical research and clinical diagnostic.This paper analyzed the development status of medical image segmentation technology, primarily discussed three commonly algorithms and their advantages and disadvantages. Such as the deformable model-based method, the region-based method, and the statistical_based method. Using 2D image sequences to generate the volume data can improve higher efficiency and accuracy of segmentation results,played an important role in the process of 3D Visualization. There are several characteristics for CT images : variation range of gray values, noise and the weak edge effects, which bring some difficulties for bone segmentation. It is necessary for us to do some further research on bone segmentation.Compared with segmentation algorithms, this thesis opted the watershed method on brain CT images. To address the problem of over_cut phenomenon, the paper modified the traditional watershed method, including the identification method and the basins merging method in the simulation process of“drown”. In addition, this thesis presented a novel method based on histogram entropy in the volum data classification, adjusting the opacity transfer function to complete the boneless of CTA dates. At last, Using Ray_Casting 3D Visualization technology to display the reconstruct result.

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