医学图像分割与配准若干关键问题研究
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摘要
医学成像技术的快速发展和影像数据的高速增长对于医学图像处理与分析领域研究起到了巨大的推动作用。其中如何量化提取和分析图像中蕴含的结构、功能和病理信息,以便辅助医生进行诊断、制定手术规划和评估治疗效果等,逐渐成为医学图像分析领域关注的热点。医学图像分割和医学图像配准正是这些研究热点的基础和前提。但由于医学成像中存在多种噪声干扰、伪影等,以及各个组织器官、病变的多样性以及个体差异性等,使得医学图像分割和配准中也存在很多难点问题。
在此背景下,为了能对医学图像进行更加鲁棒、精确的目标分割和配准,本文研究思路主要遵循以下两个方面：一是根据不同的应用背景设计适合的模型并进行改进；二是依据待分割、配准的目标来提取局部统计特征或者采用稀疏表示提取结构性特征,并结合目标的先验知识来指导模型优化过程。所取得的研究成果如下：提出了基于局部统计相似度特征的主动轮廓模型和图切分模型、基于LBP纹理特征的随机游走模型、基于分布式判别字典学习的形变模型和层次式稀疏约束下的图匹配模型。本文主要研究内容及创新之处如下：
1.为了解决医学图像在弱边界下分割泄露问题,将图像的局部统计分布特征和Bhattacharyya相似度信息相结合并引入到测地线主动轮廓模型(Geodesic Active Contour, GAC)和图切分(Graph Cuts,GC)模型的能量函数构造中。改进后GAC算法相当于为模型引入了一个基于似然比检验的回拉力,可有效阻止弱边界处泄露；基于非参数估计的能量函数构造更适用于小样本和分布函数不恒定的情况,使得改进GC模型更完整地提取图像目标的细节部分。将改进GAC和GC模型应用至膝关节MRI序列分割,提出完整分割各骨骼与半月板等结构的框架。在实验与分析部分,进行了定量与定性的实验对比。对噪声与局部体效应影响下的膝关节MRI序列及其它医学图像,结果表明所提出的方法能够有效提高分割精度。
2.针对传统随机游走图像分割方法仅考虑图像边界信息的局限性,通过求解融入纹理特征信息的对称、半正定线性方程组,提出一种新的基于随机游走的纹理图像分割算法。为了构造该方程组,首先通过局部二元模式(Local Binary Pattern,LBP)算子来描述纹理,将图像映射至不同纹理之间有显著区别的LBP图上,进而将其与梯度和几何信息结合并构造倒数型像素相似度,形成方程所需的权值矩阵,在随机游走模型下使已标号区域向未知区域传递,从而实现纹理图像分割。最后以纹理图像、噪声合成图像、MRI、CT图像为实验对象来验证算法的有效性。定性及定量实验结果表明，在多目标分割任务下,所提方法有更好的有效性和精确性。
3.针对主动外观/形状模型中需要图像外观/形状满足高斯分布假设的局限性,提出了一种新的分布式判别字典(Distributed Discriminative Dictionary,DDD)学习算法,并与形变模型结合指导MR图像中三维前列腺目标的自动分割。DDD学习算法可以非参数形式且有判别性地描述图像外观特征,从而引导形变模型的演化和分割过程。具体来说,DDD模型中主要采用了以下三种策略来提高其判别能力。首先分布式判别字典学习算法通过mRMR特征选择约束稀疏学习在一个高判别性的特征空间中。然后对稀疏学习后得到的表示残差使用线性判别分析进行进一步学习分类,提高不同类别间分类性能。最后采用“分而治之”的思想取代传统的全局字典学习,即先将形变模型沿前列腺边界进行分块,再对每块单独训练得到一个判别字典,称之为分布式学习。由于在局部区域中所蕴含的图像外观变化较小,故分布式字典学习可以更好的区分局部上的解剖组织类别。此外,在非高斯分布形状变化的情况下,使用Sparse Shape Composition(SSC)算法来描述形状统计的非参数模型,从而对形状模型先验进行建模,保持模型与形状空间的一致性。在3D前列腺MR图像库上进行实验表明,基于DDD学习的形变模型的分割结果在视觉和量化评价上均得到更好的效果。
4.在处理具有较大目标形变的医学图像对应点检测问题中,计算机视觉领域中的经典图匹配算法所得到的匹配精度往往不令人满意。针对这一问题,提出了一种新的稀疏约束下层次式图匹配算法,称为层次式稀疏图匹配算法(Hierarchical Sparse Graph Matching, HSGM)。具体来说,在层次式稀疏图匹配算法中,首先提出Line patch概念(即对局部图像块采样得到的一系列灰度剖面)来计算特征点对间的外观相似度,并与经典图匹配算法中的几何相似度相结合,提高了对应点匹配中对应点间相似度计算的可靠性。其次,在匹配概率上引入稀疏约束,实现了对错误匹配的抑制,提高了一一对应的精确性。最后将稀疏图匹配算法与层次式多参考模型框架相结合,通过采用多个参考模型和将参考模型逐渐配准至目标图像上,使得算法能够解决不同个体间具有较大解剖结构变化下的图像匹配问题,从而达到提高匹配精度和鲁棒性的目标。将HSGM算法应用于手部X光图像中进行对应点检测。实验表明,相对于其他图匹配算法,HSGM在复杂条件下匹配效果的鲁棒性和准确性更好。
The rapid development of medical imaging techniques, as well as growing amount of imaging data, has been boosting the research on medical image processing and analysis. Aiming at assisting doctors in disease diagnosis, surgery planning and therapy assessment, much efforts are made in the filed of extracting and analyzing the structural, functional and pathological information containing in medical images. Medical image segmentation and medical image registration are the fundamental tasks in this field. But there are still many challenging problems existing in these two tasks, due to the facts such as the presence of multiple noise and artifact, the variety of tissues, organs and pathology, and inter-subject variance in medical images.
In order to make the medical image segmentation and registration more robust and precise, we generally follow two roadmaps in this paper. First, we design and improve task-specific models based on different application backgrounds. Second, we formulate segmentation and registration tasks into optimization frameworks, in which we incorporate local statistical features or structural features using sparse representation and conduct the model optimization under the prior knowledge extracted from target objects. Some efficient algorithms have been proposed, such as improved active coutour model and graph cuts model based on local statistical similarity feature, an improved random walker model for texture segmentation based on LBP feature, a novel deformable model based on distributed discriminative dictionary learning, and a novel graph matching model under hierarchical and sparse framework. The primary work and contributions in the dissertation are as follows:
1. To release the leakage issue under weak boundary in medical images, statistical image features from nonparametric estimation are measured with Bhattacharyya metric, which is further embedded into energy function construction in Geodesic Active Contour (GAC) and Graph Cuts (GC) models. The improved GAC and GC model benefit from the energy function based on the aforementioned metric, which introduces a pull-back strength into GAC to prevent from boundary leaking and helps the GC model in accurately estimating distribution from small samples as well as extracting objects in detail. Then the improved methods are applied to the medical image segmentation scenario which implements a bone and meniscus segmentation framework on knee MRI sequence. In the experimental section, quantitative and qualitative comparisons are conducted respectively. Experimental results indicate the increased precision of our method in segmenting medical images such as knee MRI sequences, which are affected by the noise and the partial volume effect.
2. Observing that traditional random walker model for image segmantion only considers boundary information, we propose an improved version for texture image segmentation through solving a symmetric, semi-positive-definite system of linear equations equipped with the texture information. In the construction of the equations, we perform the feature extraction based on Local Binary Pattern (LBP) and map the original image into the space where textures are distinguished from each other (called as LBP map). The similarity between the pixels is then constructed by combining the LBP, gradient and geometric feature in a reciprocal fashion. These similarities are formed as the edge weights of the graph, which helps the labels of the seeds to be propagated to the unlabeled regions in the random walker process. Experiments on texture images, synthetic noise images and medical images shows that the proposed segmentation method extends the state-of-art random walker segmentation to texture images successfully and outperforms some other texture segmentation algorithms particularly on multi-label problem.
3. To tackle the inherent limitation of active appearance/shape model, which assumes that both shape and appearance statistics of a target object follow Gaussian distributions, we propose an Distributed Discriminative Dictionary (DDD) learning model and integrate it into a deformable model to achieve the automatic3-D prostate segmentation from MR images. The DDD model describes image appearance features in a non-parametric and discriminative fashion and thus guides the segmenting evolution of active appearance model. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First, mRMR feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second, linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third, instead of learning the global dictionaries, we design a "divide-and-conquer" learning strategy and learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. Besides, since Sparse Shape Composition (SSC) does not assume any parametric model of shape statistics, it can effectively model prostate shape priors, which may not follow a Gaussian distribution. Experiments on3D prostate MR images demonstrate the best performance of our method in terms of both visual and quantitative evaluation.
4. The state-of-the art graph matching methods usually have limited correspondence accuracy especially under the situation of large inter-subject variation. In this paper, we present a novel, hierarchical sparse graph matching (HSGM) method to further augment the power of conventional graph matching methods in establishing anatomical correspondences, especially for the cases of large inter-subject variations in medical applications. Specifically, we first propose to measure the pairwise agreement between potential correspondences along a sequence of intensity profiles (called as line patch) which reduces the ambiguity in correspondence matching. By incorporating the line patch with geometric coherence, the robustness of measuring inter-pair agreement increases. We next introduce the concept of sparsity on the fuzziness of correspondences to suppress the distraction from misleading matches, which is very important for achieving the accurate, one-to-one correspondences. Finally, we integrate our graph matching method into a hierarchical correspondence matching framework, where we use multiple models to deal with the large inter-subject anatomical variations and gradually refine the correspondence matching results between the tentatively deformed model images and the underlying subject image. Evaluations on public hand X-ray images indicate that the proposed hierarchical sparse graph matching method yields the best correspondence matching performance in terms of both accuracy and robustness when compared with several conventional graph matching methods.
在此背景下,为了能对医学图像进行更加鲁棒、精确的目标分割和配准,本文研究思路主要遵循以下两个方面：一是根据不同的应用背景设计适合的模型并进行改进；二是依据待分割、配准的目标来提取局部统计特征或者采用稀疏表示提取结构性特征,并结合目标的先验知识来指导模型优化过程。所取得的研究成果如下：提出了基于局部统计相似度特征的主动轮廓模型和图切分模型、基于LBP纹理特征的随机游走模型、基于分布式判别字典学习的形变模型和层次式稀疏约束下的图匹配模型。本文主要研究内容及创新之处如下：
1.为了解决医学图像在弱边界下分割泄露问题,将图像的局部统计分布特征和Bhattacharyya相似度信息相结合并引入到测地线主动轮廓模型(Geodesic Active Contour, GAC)和图切分(Graph Cuts,GC)模型的能量函数构造中。改进后GAC算法相当于为模型引入了一个基于似然比检验的回拉力,可有效阻止弱边界处泄露；基于非参数估计的能量函数构造更适用于小样本和分布函数不恒定的情况,使得改进GC模型更完整地提取图像目标的细节部分。将改进GAC和GC模型应用至膝关节MRI序列分割,提出完整分割各骨骼与半月板等结构的框架。在实验与分析部分,进行了定量与定性的实验对比。对噪声与局部体效应影响下的膝关节MRI序列及其它医学图像,结果表明所提出的方法能够有效提高分割精度。
2.针对传统随机游走图像分割方法仅考虑图像边界信息的局限性,通过求解融入纹理特征信息的对称、半正定线性方程组,提出一种新的基于随机游走的纹理图像分割算法。为了构造该方程组,首先通过局部二元模式(Local Binary Pattern,LBP)算子来描述纹理,将图像映射至不同纹理之间有显著区别的LBP图上,进而将其与梯度和几何信息结合并构造倒数型像素相似度,形成方程所需的权值矩阵,在随机游走模型下使已标号区域向未知区域传递,从而实现纹理图像分割。最后以纹理图像、噪声合成图像、MRI、CT图像为实验对象来验证算法的有效性。定性及定量实验结果表明，在多目标分割任务下,所提方法有更好的有效性和精确性。
3.针对主动外观/形状模型中需要图像外观/形状满足高斯分布假设的局限性,提出了一种新的分布式判别字典(Distributed Discriminative Dictionary,DDD)学习算法,并与形变模型结合指导MR图像中三维前列腺目标的自动分割。DDD学习算法可以非参数形式且有判别性地描述图像外观特征,从而引导形变模型的演化和分割过程。具体来说,DDD模型中主要采用了以下三种策略来提高其判别能力。首先分布式判别字典学习算法通过mRMR特征选择约束稀疏学习在一个高判别性的特征空间中。然后对稀疏学习后得到的表示残差使用线性判别分析进行进一步学习分类,提高不同类别间分类性能。最后采用“分而治之”的思想取代传统的全局字典学习,即先将形变模型沿前列腺边界进行分块,再对每块单独训练得到一个判别字典,称之为分布式学习。由于在局部区域中所蕴含的图像外观变化较小,故分布式字典学习可以更好的区分局部上的解剖组织类别。此外,在非高斯分布形状变化的情况下,使用Sparse Shape Composition(SSC)算法来描述形状统计的非参数模型,从而对形状模型先验进行建模,保持模型与形状空间的一致性。在3D前列腺MR图像库上进行实验表明,基于DDD学习的形变模型的分割结果在视觉和量化评价上均得到更好的效果。
4.在处理具有较大目标形变的医学图像对应点检测问题中,计算机视觉领域中的经典图匹配算法所得到的匹配精度往往不令人满意。针对这一问题,提出了一种新的稀疏约束下层次式图匹配算法,称为层次式稀疏图匹配算法(Hierarchical Sparse Graph Matching, HSGM)。具体来说,在层次式稀疏图匹配算法中,首先提出Line patch概念(即对局部图像块采样得到的一系列灰度剖面)来计算特征点对间的外观相似度,并与经典图匹配算法中的几何相似度相结合,提高了对应点匹配中对应点间相似度计算的可靠性。其次,在匹配概率上引入稀疏约束,实现了对错误匹配的抑制,提高了一一对应的精确性。最后将稀疏图匹配算法与层次式多参考模型框架相结合,通过采用多个参考模型和将参考模型逐渐配准至目标图像上,使得算法能够解决不同个体间具有较大解剖结构变化下的图像匹配问题,从而达到提高匹配精度和鲁棒性的目标。将HSGM算法应用于手部X光图像中进行对应点检测。实验表明,相对于其他图匹配算法,HSGM在复杂条件下匹配效果的鲁棒性和准确性更好。
The rapid development of medical imaging techniques, as well as growing amount of imaging data, has been boosting the research on medical image processing and analysis. Aiming at assisting doctors in disease diagnosis, surgery planning and therapy assessment, much efforts are made in the filed of extracting and analyzing the structural, functional and pathological information containing in medical images. Medical image segmentation and medical image registration are the fundamental tasks in this field. But there are still many challenging problems existing in these two tasks, due to the facts such as the presence of multiple noise and artifact, the variety of tissues, organs and pathology, and inter-subject variance in medical images.
In order to make the medical image segmentation and registration more robust and precise, we generally follow two roadmaps in this paper. First, we design and improve task-specific models based on different application backgrounds. Second, we formulate segmentation and registration tasks into optimization frameworks, in which we incorporate local statistical features or structural features using sparse representation and conduct the model optimization under the prior knowledge extracted from target objects. Some efficient algorithms have been proposed, such as improved active coutour model and graph cuts model based on local statistical similarity feature, an improved random walker model for texture segmentation based on LBP feature, a novel deformable model based on distributed discriminative dictionary learning, and a novel graph matching model under hierarchical and sparse framework. The primary work and contributions in the dissertation are as follows:
1. To release the leakage issue under weak boundary in medical images, statistical image features from nonparametric estimation are measured with Bhattacharyya metric, which is further embedded into energy function construction in Geodesic Active Contour (GAC) and Graph Cuts (GC) models. The improved GAC and GC model benefit from the energy function based on the aforementioned metric, which introduces a pull-back strength into GAC to prevent from boundary leaking and helps the GC model in accurately estimating distribution from small samples as well as extracting objects in detail. Then the improved methods are applied to the medical image segmentation scenario which implements a bone and meniscus segmentation framework on knee MRI sequence. In the experimental section, quantitative and qualitative comparisons are conducted respectively. Experimental results indicate the increased precision of our method in segmenting medical images such as knee MRI sequences, which are affected by the noise and the partial volume effect.
2. Observing that traditional random walker model for image segmantion only considers boundary information, we propose an improved version for texture image segmentation through solving a symmetric, semi-positive-definite system of linear equations equipped with the texture information. In the construction of the equations, we perform the feature extraction based on Local Binary Pattern (LBP) and map the original image into the space where textures are distinguished from each other (called as LBP map). The similarity between the pixels is then constructed by combining the LBP, gradient and geometric feature in a reciprocal fashion. These similarities are formed as the edge weights of the graph, which helps the labels of the seeds to be propagated to the unlabeled regions in the random walker process. Experiments on texture images, synthetic noise images and medical images shows that the proposed segmentation method extends the state-of-art random walker segmentation to texture images successfully and outperforms some other texture segmentation algorithms particularly on multi-label problem.
3. To tackle the inherent limitation of active appearance/shape model, which assumes that both shape and appearance statistics of a target object follow Gaussian distributions, we propose an Distributed Discriminative Dictionary (DDD) learning model and integrate it into a deformable model to achieve the automatic3-D prostate segmentation from MR images. The DDD model describes image appearance features in a non-parametric and discriminative fashion and thus guides the segmenting evolution of active appearance model. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First, mRMR feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second, linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third, instead of learning the global dictionaries, we design a "divide-and-conquer" learning strategy and learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. Besides, since Sparse Shape Composition (SSC) does not assume any parametric model of shape statistics, it can effectively model prostate shape priors, which may not follow a Gaussian distribution. Experiments on3D prostate MR images demonstrate the best performance of our method in terms of both visual and quantitative evaluation.
4. The state-of-the art graph matching methods usually have limited correspondence accuracy especially under the situation of large inter-subject variation. In this paper, we present a novel, hierarchical sparse graph matching (HSGM) method to further augment the power of conventional graph matching methods in establishing anatomical correspondences, especially for the cases of large inter-subject variations in medical applications. Specifically, we first propose to measure the pairwise agreement between potential correspondences along a sequence of intensity profiles (called as line patch) which reduces the ambiguity in correspondence matching. By incorporating the line patch with geometric coherence, the robustness of measuring inter-pair agreement increases. We next introduce the concept of sparsity on the fuzziness of correspondences to suppress the distraction from misleading matches, which is very important for achieving the accurate, one-to-one correspondences. Finally, we integrate our graph matching method into a hierarchical correspondence matching framework, where we use multiple models to deal with the large inter-subject anatomical variations and gradually refine the correspondence matching results between the tentatively deformed model images and the underlying subject image. Evaluations on public hand X-ray images indicate that the proposed hierarchical sparse graph matching method yields the best correspondence matching performance in terms of both accuracy and robustness when compared with several conventional graph matching methods.
引文
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