一种脑MRI图像的混合分割模型
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摘要
MRI作为医学领域中一种重要的成像技术,具有辐射小、分辨率高以及对软组织分辨力强的特点,为人脑的研究提供了一种有效的途径,并已广泛应用于医学、神经科学、认知科学和心理学等方面的研究和临床应用中。脑灰质、脑白质与脑脊液是三种主要的脑组织,脑部的老化和病理常常与这三种组织的体积、形态等特征变化相联系。对这三种组织准确、快速的分割是后续可视化、量化的诊断和分析、手术规划、治疗跟踪与评估等处理和操作的基础,无论对于医学研究还是临床应用都具有十分重要的意义。
但是由于技术上的原因,以及成像条件上的限制,MRI图像的质量常常受到噪声、局部体效应和偏移场效应的影响,呈现出模糊和灰度均匀性的特点。此外,人脑解剖结构复杂、个体间的差异大、临床应用对医学图像分割的精确度和算法执行的速度要求高,现有的算法还远远没有达到理想的效果。因此,脑MRI图像的分割仍旧是医学图像分割领域中的热点问题。
本文重点研究了基于分水岭变换的分割算法和基于可形变模型的分割算法,提出了一种基于优化的分水岭变换和Chan-Vese模型的混合分割模型,本文的主要工作和创新点包括以下几个方面:
1.针对分水岭变换的过分割问题,我们提出了一种优化的分水岭变换算法。相对于传统基于前处理或后处理的改进方法,我们通过引入一个差异度函数,将逐层浸没的方式修改成为多层次浸没的方式,在分水岭变换的执行过程中对过分割进行抑制。这种方式能够有效的利用分水岭变换过程的中间结果,同时避免了预处理或后处理带来的额外开销。此外,我们在多层次浸没的框架中加入了对边缘线显著性的限制,保证在抑制过分割的同时不会破坏感兴趣区域的边界。实验证明,本文提出的优化的分水岭算法,在过分割抑制、分割速度和分割精确度等方面的性能都有显著的提高,不仅解决了传统的分水岭变换中的过分割问题,而且显示出较ITK的优化分水岭变换算法更高的分割精确度。为后续与几何形变模型的结合打下了坚实的基础。
2.针对分水岭变换分割结果平滑性差的问题,我们提出了一种基于优化的分水岭变换与CV模型的混合分割模型。在该模型中,我们使用Chan-Vese模型对优化的分水岭算法得到的感兴趣区域的初始轮廓进行演化。这样的结合,一方面解决了分水岭变换分割结果平滑性差的问题;另一方面克服了Chan-Vese模型对初始轮廓位置敏感、计算复杂以及收敛速度慢的弱点。能够在不增加计算复杂度的情况下进一步提高分割的精确度。
3.在混合分割模型的基础上,设计并实现了一套分割系统,并将该系统应用于实际的脑MRI图像数据的分割。实现了非脑组织与脑组织的分离,以及脑灰质、脑白质和脑脊液的分割,取得了较为满意的结果。实验结果证明,本文提出的混合分割模型无论在分割的精确度、分割的速度,还是自动化程度方面都有显著的提高。
As one of the leading techniques for medical imaging, MRI provides a useful tool for human brain research, and has been widely used in medicine, neuroscience, cognitive science,psychology, etc. Grey matter, white matter and cerebrospinal fluid (CFS) are three major brain tissues. Accurate segmentation of these three tissues has both clinical and academic significance.Accurate measurement of the volume and morphology of these tissues can help early diagnosis of pathology, as well as understanding the way they change during development,aging, and pathology. Besides, accurate measurement and localization of these tissue, are prerequisite of subsequent analysis, such as visualization, surgical planning, computer integrated surgery, treatment tracing, etc.
However, the performance of automatic segmentation of brain MRI images is still hindered by following factors:The complexity nature of the brain anatomy, as well as the inter and intra variations of the these structure bring diffculty to brain modeling; Intensity inhomogeneity caused by inhomogeneity of magnetic field and biological variation within the same tissue;Partial volume effect. Therefore, the existing methods are still far from the clinical requirement for segmentation accuracy, speed, and automation. Therefore, automatic segmentation of brain MRI is still a challenge work.
In this thesis, a hybrid model for medical image segmentation, which combines an improved watershed transform and geometric deformable model is proposed. Achievements and innovation points in this thesis are described below:
1.We proposed a saliency measurement constrained multilevel immersion watershed transform method. The innovation of our method is that, in contrast to the traditional preprocessing and postprocessing method used to overcome oversegmenation, our method can suppress the oversegmenation during the watershed transform. Besides, additional saliency measurement was added as constraint to avoid important contours from being destroyed. Experiment results demonstrate the superior performance of our method over the traditional watershed method and the improved watershed method provided by ITK, in terms of segmentation accuracy and speed,and thus lays a concrete foundation for the subsequent combination with geometric model.
2.Chan-Vese model was used to propagate the initial contour of object of interest which produced by our improved watershed transform, in order to correct its smoothness. This combination can avoid manual initial contour specifying, improve the speed of convergence, and further segmentation accuracy.
3.A segmentation system was designed and realized for the proposed hybrid segmentation system, and was tested on real brain MRI image. We provide experiment of brain skull stripping, as well as grey matter, white matter and cerebrospinal fluid (CFS) segmentation to explain the effectiveness of these proposed methods in terms of segmentation accuracy, time complexity and automation.
但是由于技术上的原因,以及成像条件上的限制,MRI图像的质量常常受到噪声、局部体效应和偏移场效应的影响,呈现出模糊和灰度均匀性的特点。此外,人脑解剖结构复杂、个体间的差异大、临床应用对医学图像分割的精确度和算法执行的速度要求高,现有的算法还远远没有达到理想的效果。因此,脑MRI图像的分割仍旧是医学图像分割领域中的热点问题。
本文重点研究了基于分水岭变换的分割算法和基于可形变模型的分割算法,提出了一种基于优化的分水岭变换和Chan-Vese模型的混合分割模型,本文的主要工作和创新点包括以下几个方面:
1.针对分水岭变换的过分割问题,我们提出了一种优化的分水岭变换算法。相对于传统基于前处理或后处理的改进方法,我们通过引入一个差异度函数,将逐层浸没的方式修改成为多层次浸没的方式,在分水岭变换的执行过程中对过分割进行抑制。这种方式能够有效的利用分水岭变换过程的中间结果,同时避免了预处理或后处理带来的额外开销。此外,我们在多层次浸没的框架中加入了对边缘线显著性的限制,保证在抑制过分割的同时不会破坏感兴趣区域的边界。实验证明,本文提出的优化的分水岭算法,在过分割抑制、分割速度和分割精确度等方面的性能都有显著的提高,不仅解决了传统的分水岭变换中的过分割问题,而且显示出较ITK的优化分水岭变换算法更高的分割精确度。为后续与几何形变模型的结合打下了坚实的基础。
2.针对分水岭变换分割结果平滑性差的问题,我们提出了一种基于优化的分水岭变换与CV模型的混合分割模型。在该模型中,我们使用Chan-Vese模型对优化的分水岭算法得到的感兴趣区域的初始轮廓进行演化。这样的结合,一方面解决了分水岭变换分割结果平滑性差的问题;另一方面克服了Chan-Vese模型对初始轮廓位置敏感、计算复杂以及收敛速度慢的弱点。能够在不增加计算复杂度的情况下进一步提高分割的精确度。
3.在混合分割模型的基础上,设计并实现了一套分割系统,并将该系统应用于实际的脑MRI图像数据的分割。实现了非脑组织与脑组织的分离,以及脑灰质、脑白质和脑脊液的分割,取得了较为满意的结果。实验结果证明,本文提出的混合分割模型无论在分割的精确度、分割的速度,还是自动化程度方面都有显著的提高。
As one of the leading techniques for medical imaging, MRI provides a useful tool for human brain research, and has been widely used in medicine, neuroscience, cognitive science,psychology, etc. Grey matter, white matter and cerebrospinal fluid (CFS) are three major brain tissues. Accurate segmentation of these three tissues has both clinical and academic significance.Accurate measurement of the volume and morphology of these tissues can help early diagnosis of pathology, as well as understanding the way they change during development,aging, and pathology. Besides, accurate measurement and localization of these tissue, are prerequisite of subsequent analysis, such as visualization, surgical planning, computer integrated surgery, treatment tracing, etc.
However, the performance of automatic segmentation of brain MRI images is still hindered by following factors:The complexity nature of the brain anatomy, as well as the inter and intra variations of the these structure bring diffculty to brain modeling; Intensity inhomogeneity caused by inhomogeneity of magnetic field and biological variation within the same tissue;Partial volume effect. Therefore, the existing methods are still far from the clinical requirement for segmentation accuracy, speed, and automation. Therefore, automatic segmentation of brain MRI is still a challenge work.
In this thesis, a hybrid model for medical image segmentation, which combines an improved watershed transform and geometric deformable model is proposed. Achievements and innovation points in this thesis are described below:
1.We proposed a saliency measurement constrained multilevel immersion watershed transform method. The innovation of our method is that, in contrast to the traditional preprocessing and postprocessing method used to overcome oversegmenation, our method can suppress the oversegmenation during the watershed transform. Besides, additional saliency measurement was added as constraint to avoid important contours from being destroyed. Experiment results demonstrate the superior performance of our method over the traditional watershed method and the improved watershed method provided by ITK, in terms of segmentation accuracy and speed,and thus lays a concrete foundation for the subsequent combination with geometric model.
2.Chan-Vese model was used to propagate the initial contour of object of interest which produced by our improved watershed transform, in order to correct its smoothness. This combination can avoid manual initial contour specifying, improve the speed of convergence, and further segmentation accuracy.
3.A segmentation system was designed and realized for the proposed hybrid segmentation system, and was tested on real brain MRI image. We provide experiment of brain skull stripping, as well as grey matter, white matter and cerebrospinal fluid (CFS) segmentation to explain the effectiveness of these proposed methods in terms of segmentation accuracy, time complexity and automation.
引文
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