基于DR图像的股骨个体化姿态估计关键技术研究
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摘要
股骨干骨折是骨科一种常见的临床创伤，如果手术治疗不当，会造成患者行走功能障碍、肢体变形等后遗症，严重影响患者的生活质量。在现代医学诊断中，常见的临床三维影像检查方式主要包括计算机断层扫描（CT）和磁共振成像（MRI）。这两种成像手段虽然能够直接获得患肢的三维数据，但是在检查费用、操作便利性、成像快捷性等方面都远逊于数字X线（DR）成像，特别是CT扫描过程的有害辐射剂量远高于DR成像。然而DR图像是二维投影图像，缺少三维空间信息进行临床诊断是DR成像的最大弊端。因此，研究一种仅利用单张或少数有限张二维DR图像，在手术前获取股骨三维空间姿态的成像方法，具有极其重要的临床实用价值和广阔的市场应用前景。
本文旨在利用股骨DR图像，建立一种能够快速、有效地重建股骨个体化姿态的方法，进而实现股骨三维空间姿态可视化。针对姿态估计过程中存在的DR成像系统标定、灰度不均匀图像特征轮廓提取、基于2D-3D的图像配准以及二维点集配准过程中多极值优化等关键问题，研究相应的解决方法。并深入研究灰度不均匀图像分割、多极值优化和2D-3D图像配准方法，这三项关键技术，从而实现术前股骨个体化姿态的三维重建，促进基于图像引导的三维可视化技术的发展。论文的主要研究内容包括：
针对普通平面标定板在X射线环境下成像不清晰，无法实现DR成像系统标定的问题，利用覆铜线格PCB板做为标定板，采用基于平面标定板标定法，获得DR成像系统的标定参数。
针对骨折股骨DR图像拓扑结构复杂、灰度分布不均匀的特点，研究一种结合灰度波动信息的活动轮廓模型，利用灰度波动概念获得图像的灰度波动曲线；依据对波动曲线单调区间的判断结果，对图像进行灰度波动变换；将灰度波动变换函数引入Chan-Vese（C-V）模型并重新定义能量函数，获得新的活动轮廓模型。通过人造图像与医学图像的分割实验表明，该模型既能继承C-V模型抗噪性强、运算复杂度低、对初始轮廓不敏感的优点，又可克服C-V模型不能分割灰度分布不均匀图像的弊端，并以较高的分割精度提取出DR图像中股骨干内外腔壁和骨折断端完整、连续的特征轮廓。
点集配准问题实质上是一个多极值优化问题，常规的智能优化算法存在容易陷入局部极值和搜索效率不高的问题。针对此类问题，将一种全局性优化算法——特征统计算法（CSA）应用到点集配准中，研究一种基于高斯混合模型CSA优化的点集配准算法。通过重新组合高斯混合模型中的配准参数，确定与目标函数关系密切的特征统计项目，能够更好地解决点集配准中多参数估计易陷入局部极值的问题，提高算法的搜索效率。测试数据配准实验结果表明，该配准算法具有良好的抗噪声、抗缺失点、抗出格点能力，同时具有较高的配准精度和配准成功率。
针对股骨DR图像缺少临床诊断所需的三维空间信息的问题，研究一种基于双平面DR图像2D-3D仿射配准的姿态估计方法。在完成股骨特征轮廓提取的基础上，利用点集配准算法获得正、侧位股骨DR图像特征轮廓与通用股骨模型投影轮廓之间的二维配准参数；利用相机标定法所确定的二维平面与三维空间的变换关系，获得三维变换矩阵，并作用到股骨通用模型上获得股骨个体化姿态。配准实验表明该方法能够在缺少个体化三维信息情况下，仅利用股骨正、侧位DR图像信息及股骨通用模型，重建出股骨个体化姿态。
Femoral shaft fracture is a common clinical orthopedic trauma. Walk dysfunction, limb deformity or other consequences may happen to the patients if it is not treated properly, which will significantly affect patients’ life quality. In modern medical diagnosis, the common clinical approaches for three-dimensional (3D) imaging mainly include computed tomography (CT) and magnetic resonance imaging (MRI). Although the3D data of limbs can be obtained directly by these two imaging methods, the cost, convenience in operation, imaging speed, and harmful radiation amount are inferior to that of digital radiograph (DR) imaging. However, DR images are2D projected images, and lacking of3D spatial information is the biggest shortcoming in clinical diagnosis with DR imaging equipment. Therefore, developing one approach for recovering3D pose of femur before surgery only by using one single or few limited two-dimensional (2D) DR images is of extremely clinical values and broad application prospects.
This dissertation aims to design a method for quickly and effectively reconstructing femur patient-specific pose by using femoral DR images, and then realize description of femur in3D space. In this dissertation, corresponding solutions have been studied to handle the existed key problems such as DR imaging system calibration, feature extraction issue of inhomogeneous intensity distribution image,2D-3D image registration, and multiple extremum optimization of2D point set registration, which realizes patient-specific pose visualization of3D preoperative femoral fracture. It is studied deeply about three key technologies which are segmentation of inhomogeneous intensity distribution image, multiple extremum optimization problem and2D-3D image registration. The results promote the image-based guided visualization technology. The main contents of this dissertation are as follows:
DR imaging system cannot be calibrated since images got from common plane calibration board are not clear under condition of X-rays. To handle this problem, PCB board containing copper grid was used to be new calibration board, the same calibration method based on plane calibration board was applied, and finally we obtained calibration parameters for the DR imaging system.
Femoral fracture DR image is of complicated topology and inhomogeneous intensity distribution. To respond such kind characteristics, a new active contour model combined with grayscale fluctuation information was proposed. In details, we get grayscale fluctuation curve of an image basing on the concept of grayscale fluctuation; taking grayscale fluctuation transform to the image according to the judgment on the fluctuation range of a monotonic curve; introducing grayscale fluctuation transform function into a Chan-Vese (C-V) model and redefining the energy function, we finally obtain the new active contour model. Synthetic and medical image segmentation experiments showed that this new model not only inherits characteristics of perfect anti-noise performance, low computational complexity, initial contour insensitivity, etc., but also overcomes the shortcoming that C-V model cannot segments inhomogeneous intensity images. In addition, the new model can extract intact and continuous characteristic contour of femur fracture and marrow cavity in DR image with high accuracy.
Point set registration is essentially a multiple extremum optimization process. Traditional intelligent optimization algorithms easily fall into local minimum and are inefficient in searching when they are used for complicated or special optimization problems. To address this issue, a characteristics statistical algorithm (CSA) was adopted to the point set registration and we proposed a point registration algorithm based on Gaussian mixture model (GMM). After determining the characteristics statistical items which are closely related to target function through recombining the registration parameters, it will be better to address the problem that parameter estimation of point set registration easily fall into local minimum and improve the efficiency of the algorithm in searching. Registration results of testing data proved that the proposed algorithm has good anti-noise, anti-point-missing and outliers resistant abilities. This algorithm is of higher registration accuracy and success rate for registration.
To address the limitation of lacking of3D spatial information in DR images for clinical diagnosis, a3D pose estimation method based on affine2D-3D image registration using bi-planar DR images was proposed. After extracting characteristic contour of femur, we got2D registration parameters between segmented contours of DR image and projected contours of femur generic model by using point set registration. We further obtained3D pose matrix which is got through determining the transform relationship between2D plane and3D space based on pinhole calibration method. And then applying the pose matrix to generic model we finally obtain the patient-specific femoral pose. Experimental results showed that, under condition of lacking of3D patient-specific spatial information, this method can reconstruct patient-specific femur pose which is only depending on DR images from anteroposterior and lateral views with femoral generic model.
本文旨在利用股骨DR图像，建立一种能够快速、有效地重建股骨个体化姿态的方法，进而实现股骨三维空间姿态可视化。针对姿态估计过程中存在的DR成像系统标定、灰度不均匀图像特征轮廓提取、基于2D-3D的图像配准以及二维点集配准过程中多极值优化等关键问题，研究相应的解决方法。并深入研究灰度不均匀图像分割、多极值优化和2D-3D图像配准方法，这三项关键技术，从而实现术前股骨个体化姿态的三维重建，促进基于图像引导的三维可视化技术的发展。论文的主要研究内容包括：
针对普通平面标定板在X射线环境下成像不清晰，无法实现DR成像系统标定的问题，利用覆铜线格PCB板做为标定板，采用基于平面标定板标定法，获得DR成像系统的标定参数。
针对骨折股骨DR图像拓扑结构复杂、灰度分布不均匀的特点，研究一种结合灰度波动信息的活动轮廓模型，利用灰度波动概念获得图像的灰度波动曲线；依据对波动曲线单调区间的判断结果，对图像进行灰度波动变换；将灰度波动变换函数引入Chan-Vese（C-V）模型并重新定义能量函数，获得新的活动轮廓模型。通过人造图像与医学图像的分割实验表明，该模型既能继承C-V模型抗噪性强、运算复杂度低、对初始轮廓不敏感的优点，又可克服C-V模型不能分割灰度分布不均匀图像的弊端，并以较高的分割精度提取出DR图像中股骨干内外腔壁和骨折断端完整、连续的特征轮廓。
点集配准问题实质上是一个多极值优化问题，常规的智能优化算法存在容易陷入局部极值和搜索效率不高的问题。针对此类问题，将一种全局性优化算法——特征统计算法（CSA）应用到点集配准中，研究一种基于高斯混合模型CSA优化的点集配准算法。通过重新组合高斯混合模型中的配准参数，确定与目标函数关系密切的特征统计项目，能够更好地解决点集配准中多参数估计易陷入局部极值的问题，提高算法的搜索效率。测试数据配准实验结果表明，该配准算法具有良好的抗噪声、抗缺失点、抗出格点能力，同时具有较高的配准精度和配准成功率。
针对股骨DR图像缺少临床诊断所需的三维空间信息的问题，研究一种基于双平面DR图像2D-3D仿射配准的姿态估计方法。在完成股骨特征轮廓提取的基础上，利用点集配准算法获得正、侧位股骨DR图像特征轮廓与通用股骨模型投影轮廓之间的二维配准参数；利用相机标定法所确定的二维平面与三维空间的变换关系，获得三维变换矩阵，并作用到股骨通用模型上获得股骨个体化姿态。配准实验表明该方法能够在缺少个体化三维信息情况下，仅利用股骨正、侧位DR图像信息及股骨通用模型，重建出股骨个体化姿态。
Femoral shaft fracture is a common clinical orthopedic trauma. Walk dysfunction, limb deformity or other consequences may happen to the patients if it is not treated properly, which will significantly affect patients’ life quality. In modern medical diagnosis, the common clinical approaches for three-dimensional (3D) imaging mainly include computed tomography (CT) and magnetic resonance imaging (MRI). Although the3D data of limbs can be obtained directly by these two imaging methods, the cost, convenience in operation, imaging speed, and harmful radiation amount are inferior to that of digital radiograph (DR) imaging. However, DR images are2D projected images, and lacking of3D spatial information is the biggest shortcoming in clinical diagnosis with DR imaging equipment. Therefore, developing one approach for recovering3D pose of femur before surgery only by using one single or few limited two-dimensional (2D) DR images is of extremely clinical values and broad application prospects.
This dissertation aims to design a method for quickly and effectively reconstructing femur patient-specific pose by using femoral DR images, and then realize description of femur in3D space. In this dissertation, corresponding solutions have been studied to handle the existed key problems such as DR imaging system calibration, feature extraction issue of inhomogeneous intensity distribution image,2D-3D image registration, and multiple extremum optimization of2D point set registration, which realizes patient-specific pose visualization of3D preoperative femoral fracture. It is studied deeply about three key technologies which are segmentation of inhomogeneous intensity distribution image, multiple extremum optimization problem and2D-3D image registration. The results promote the image-based guided visualization technology. The main contents of this dissertation are as follows:
DR imaging system cannot be calibrated since images got from common plane calibration board are not clear under condition of X-rays. To handle this problem, PCB board containing copper grid was used to be new calibration board, the same calibration method based on plane calibration board was applied, and finally we obtained calibration parameters for the DR imaging system.
Femoral fracture DR image is of complicated topology and inhomogeneous intensity distribution. To respond such kind characteristics, a new active contour model combined with grayscale fluctuation information was proposed. In details, we get grayscale fluctuation curve of an image basing on the concept of grayscale fluctuation; taking grayscale fluctuation transform to the image according to the judgment on the fluctuation range of a monotonic curve; introducing grayscale fluctuation transform function into a Chan-Vese (C-V) model and redefining the energy function, we finally obtain the new active contour model. Synthetic and medical image segmentation experiments showed that this new model not only inherits characteristics of perfect anti-noise performance, low computational complexity, initial contour insensitivity, etc., but also overcomes the shortcoming that C-V model cannot segments inhomogeneous intensity images. In addition, the new model can extract intact and continuous characteristic contour of femur fracture and marrow cavity in DR image with high accuracy.
Point set registration is essentially a multiple extremum optimization process. Traditional intelligent optimization algorithms easily fall into local minimum and are inefficient in searching when they are used for complicated or special optimization problems. To address this issue, a characteristics statistical algorithm (CSA) was adopted to the point set registration and we proposed a point registration algorithm based on Gaussian mixture model (GMM). After determining the characteristics statistical items which are closely related to target function through recombining the registration parameters, it will be better to address the problem that parameter estimation of point set registration easily fall into local minimum and improve the efficiency of the algorithm in searching. Registration results of testing data proved that the proposed algorithm has good anti-noise, anti-point-missing and outliers resistant abilities. This algorithm is of higher registration accuracy and success rate for registration.
To address the limitation of lacking of3D spatial information in DR images for clinical diagnosis, a3D pose estimation method based on affine2D-3D image registration using bi-planar DR images was proposed. After extracting characteristic contour of femur, we got2D registration parameters between segmented contours of DR image and projected contours of femur generic model by using point set registration. We further obtained3D pose matrix which is got through determining the transform relationship between2D plane and3D space based on pinhole calibration method. And then applying the pose matrix to generic model we finally obtain the patient-specific femoral pose. Experimental results showed that, under condition of lacking of3D patient-specific spatial information, this method can reconstruct patient-specific femur pose which is only depending on DR images from anteroposterior and lateral views with femoral generic model.
引文
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