医学超声图像的轮廓波方法研究及其在相控HIFU治疗系统中的应用
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摘要
近年来，相控高强度聚焦超声（HIFU, High Intensity Focused Ultrasound）技术已经成为治疗超声的研究热点。HIFU强度较高，为了避免损伤正常组织和提高治疗效率，必须提供治疗目标的精确位置。在目前超声图像引导的HIFU治疗系统中，超声图像由于受散斑噪声等降质因素影响分辨率较低，是达到HIFU精确治疗的障碍之一。另外，现在大部分已投入使用的HIFU系统未能充分考虑呼吸运动在治疗中造成的病灶等治疗目标的移位，也影响了HIFU的精确治疗。利用术中超声图像的实时处理达到通过体内标记来实时定位治疗目标，是提高临床治疗准确性与快速性的一种行之有效的方法。超声图像的预处理效果是定位准确的一个关键因素。本研究将目前图像领域的新进展之一——轮廓波引入对超声图像的处理，通过对图像引导的关键技术的研究促进相控HIFU精确治疗的发展。
目前轮廓波在图像处理领域发展迅速，尤其对去噪、轮廓提取和纹理分析等应用的研究，都取得了相当的进展。轮廓波是小波在二维和高维数据处理的新发展，是多尺度几何分析方法之一。本文的研究集中于轮廓波在超声图像处理应用的适应性改进上，包括图像变换域统计建模、超声图像散斑抑制、边缘曲线特征的检测，给出改进的适用于超声图像处理的轮廓波方法，主要内容如下：
(1)对轮廓波变换的构造原理以及变换域系数的统计分析与建模进行系统分析，提出一种针对图像变换域统计特性的建模方法，对图像轮廓波域建模所依赖的广义高斯分布函数加以扩展和改进。由于传统的广义高斯分布函数对非对称、尖峰或严重拖尾等分布难于精确建模，本文构建了非对称分段广义高斯函数（APGGF），并对其进行统计参数估计，以非对称分段特性实现对统计直方图的更精确拟合。在轮廓波变换域对医学超声图像统计建模，利用APGGF对超声图像轮廓波系数进行拟合，与广义高斯函数相比，逼近误差降低，提高了统计建模精度。
(2)根据超声图像散斑噪声的一般模型，提出轮廓波域尺度自适应超声图像阈值去噪方法，通过对轮廓波域不同尺度不同子带系数进行阈值处理以实现对噪声分量的去除。同时，加入尺度子带自适应比例参数并给出经验计算公式，使之适于医学超声图像。
(3)对超声图像中散斑噪声分布统计特性进行研究，提出基于对噪声分量用Rayleigh分布建模的轮廓波域超声图像散斑噪声抑制方法，该方法在贝叶斯框架下利用Laplacian分布对轮廓波域信号分量建模，通过MAP准则推导出轮廓波系数的估算子，通过模拟图像与临床图像的实验与经典方法的比较，验证其性能的改进；由于广义Nakagami分布(GND)对散斑噪声的统计特性描述更具通用性，本文提出一种基于GND的非同态轮廓波散斑抑制方法，并给出对GND几个退化模型的特例分析，通过临床图像实验与前人方法进行比较验证了算法有效性及性能改进。这两种基于不同噪声统计模型的方法实现了有针对性的噪声滤除。
(4)为实现相控HIFU治疗在术中对病灶的实时跟踪检测，本文对超声图像中边缘轮廓等曲线特征检测和提取，并根据这些特征信息对呼吸运动影响下的器官病灶移位做实时检测和定位。本文提出基于子带系数模极大值的轮廓波域医学超声图像边缘轮廓检测方法。该方法更有效的提取肿瘤状轮廓特征边缘和血管、脏器边界等曲线状特征边缘，而这些特征信息可以作为呼吸运动影响下的器官病灶移位的实时检测和定位的体内跟踪标记。由于轮廓波固有的对平滑边缘的稀疏表达，降低了运算复杂度。
(5)根据相控HIFU治疗中治疗探头与成像探头的结合特点，提出利用术中超声成像，通过同轴旋转扫描实现治疗目标在三维治疗系统中的定位。通过术中超声图像处理与电子相控聚焦的治疗焦点转换移位的实时控制，实现超声成像系统到治疗系统的目标定位。
High Intensity Focused Ultrasound (HIFU) has been explored for its therapeuticuse in the treatment of tumors. The main advantage of HIFU is its non-invasive nature,and the focus where therapy occurs can be placed deep within a patient’s bodywithout affecting the intervening tissue layers. The localized effects of HIFU and itsaccurate focusing capability make it an attractive non-invasive surgical modality. Inthe interactive image-guided HIFU therapy, fast and precise target localization is veryimportant for treatment planning. Ultrasound image guidance of HIFU therapy hasbeen used because of its portability, low cost, real time, simple integration with HIFUinstruments. Therefore, the use of US visualization for the guidance and monitoring ofHIFU therapies most often relies on the performance of the US image processing.
Contourlet transform was introduced as a discrete domain multiresolution andmultidirection expansion using non-separable filter banks, and developed as a “true”two-dimensional representation for images. Contourlet is considered to be the newgeneration of wavelet in two and higher dimensions. It not only has themultiresolution and time-frequency localization properties, but also shows a very highdegree of directionality and anisotropy. Motivated by and capitalizing on this property,contourlet transform can be applied in a wide range of image processing tasks, such asdenoising, and has shown its potential in the field of medical image processing.
The objectives of this dissertation were to introduce contourlet analysistechniques into the ultrasound image processing in HIFU system and make properadaption to clinical applications, including image statistical modeling, ultrasoundspeckle suppressing and treatment target edge detection for real-time HIFU therapy.
Firstly, to further analyse the statistical characteristics of the contourletcoefficients for ultrasound image, we construct a new asymmetric piecewisegeneralized Gaussian function (APGGF) for image statistical modeling in transformdomain. By fitting the statistical probability more precisely and giving thecorresponding parametric estimation, this parametric modeling method has potentialfor many image processing applications, such as image coding, feature extraction,image denoising, and so on.
Then we present a new contourlet-based speckle reduction method for medical ultrasound images. This method gives a scale-adaptive threshold in Bayesianframework based on modeling the subband contourlet coefficients of the ultrasoundimages after logarithmic transform as generalized Gaussian distribution. An adjustedproportional parameter is proposed for adapting the threshold to medical ultrasoundimages in contourlet domain. According to its less computing time, this ultrasoundimage pre-precessing algorithm can satisfy the HIFU intra-surgery requirement.
For more precisely preprocessing the ultrasound images, a new specklesuppression method for medical ultrasound images based on contourlet transform wasproposed. Modeling the speckle with Rayleigh distribution in logarithmicallytransformed ultrasound images, a maximum a posterior (MAP) estimator is appliedfor speckle reduction via manipulating the coefficients in contourlet domain. Asgeneralized Nakagami distribution provides a better model for the statistic of speckledue to its capability and generality, we also proposed another speckle suppressionmethod according to generalized Nakagami distribution model, and further analysiswere then given for several special cases.
A contourlet based edge detection method was then given to extracting theinside-body mark from ultrasound images for phased HIFU intra-surgery targetlocalization. This method extracts the curve structure in images by detecting modulusmaxima in different scale and different directional coutourlet subbands. Thecharacteristic of multiresolution and multidirection, and the low computationcomplexity make the contourlet based edge detection method a choice for phasedHIFU intra-surgery image processing.
Finally, we discussed the affection of the respiration in HIFU therapy system andthe respiration control techniques in image guided radiotherapy. For HIFUintra-surgery target tracking, there are two key factors: the effectiveness ofinside-body mark detection in real time to locating the treatment target; the positionshift of the phased HIFU lesion is also fast enough to make tracking treatmentpossible. According to the characteristics of the image series scanning mode duringthe surgery system, a target localization method is then given in our clinical phasedHIFU therapy system.
目前轮廓波在图像处理领域发展迅速，尤其对去噪、轮廓提取和纹理分析等应用的研究，都取得了相当的进展。轮廓波是小波在二维和高维数据处理的新发展，是多尺度几何分析方法之一。本文的研究集中于轮廓波在超声图像处理应用的适应性改进上，包括图像变换域统计建模、超声图像散斑抑制、边缘曲线特征的检测，给出改进的适用于超声图像处理的轮廓波方法，主要内容如下：
(1)对轮廓波变换的构造原理以及变换域系数的统计分析与建模进行系统分析，提出一种针对图像变换域统计特性的建模方法，对图像轮廓波域建模所依赖的广义高斯分布函数加以扩展和改进。由于传统的广义高斯分布函数对非对称、尖峰或严重拖尾等分布难于精确建模，本文构建了非对称分段广义高斯函数（APGGF），并对其进行统计参数估计，以非对称分段特性实现对统计直方图的更精确拟合。在轮廓波变换域对医学超声图像统计建模，利用APGGF对超声图像轮廓波系数进行拟合，与广义高斯函数相比，逼近误差降低，提高了统计建模精度。
(2)根据超声图像散斑噪声的一般模型，提出轮廓波域尺度自适应超声图像阈值去噪方法，通过对轮廓波域不同尺度不同子带系数进行阈值处理以实现对噪声分量的去除。同时，加入尺度子带自适应比例参数并给出经验计算公式，使之适于医学超声图像。
(3)对超声图像中散斑噪声分布统计特性进行研究，提出基于对噪声分量用Rayleigh分布建模的轮廓波域超声图像散斑噪声抑制方法，该方法在贝叶斯框架下利用Laplacian分布对轮廓波域信号分量建模，通过MAP准则推导出轮廓波系数的估算子，通过模拟图像与临床图像的实验与经典方法的比较，验证其性能的改进；由于广义Nakagami分布(GND)对散斑噪声的统计特性描述更具通用性，本文提出一种基于GND的非同态轮廓波散斑抑制方法，并给出对GND几个退化模型的特例分析，通过临床图像实验与前人方法进行比较验证了算法有效性及性能改进。这两种基于不同噪声统计模型的方法实现了有针对性的噪声滤除。
(4)为实现相控HIFU治疗在术中对病灶的实时跟踪检测，本文对超声图像中边缘轮廓等曲线特征检测和提取，并根据这些特征信息对呼吸运动影响下的器官病灶移位做实时检测和定位。本文提出基于子带系数模极大值的轮廓波域医学超声图像边缘轮廓检测方法。该方法更有效的提取肿瘤状轮廓特征边缘和血管、脏器边界等曲线状特征边缘，而这些特征信息可以作为呼吸运动影响下的器官病灶移位的实时检测和定位的体内跟踪标记。由于轮廓波固有的对平滑边缘的稀疏表达，降低了运算复杂度。
(5)根据相控HIFU治疗中治疗探头与成像探头的结合特点，提出利用术中超声成像，通过同轴旋转扫描实现治疗目标在三维治疗系统中的定位。通过术中超声图像处理与电子相控聚焦的治疗焦点转换移位的实时控制，实现超声成像系统到治疗系统的目标定位。
High Intensity Focused Ultrasound (HIFU) has been explored for its therapeuticuse in the treatment of tumors. The main advantage of HIFU is its non-invasive nature,and the focus where therapy occurs can be placed deep within a patient’s bodywithout affecting the intervening tissue layers. The localized effects of HIFU and itsaccurate focusing capability make it an attractive non-invasive surgical modality. Inthe interactive image-guided HIFU therapy, fast and precise target localization is veryimportant for treatment planning. Ultrasound image guidance of HIFU therapy hasbeen used because of its portability, low cost, real time, simple integration with HIFUinstruments. Therefore, the use of US visualization for the guidance and monitoring ofHIFU therapies most often relies on the performance of the US image processing.
Contourlet transform was introduced as a discrete domain multiresolution andmultidirection expansion using non-separable filter banks, and developed as a “true”two-dimensional representation for images. Contourlet is considered to be the newgeneration of wavelet in two and higher dimensions. It not only has themultiresolution and time-frequency localization properties, but also shows a very highdegree of directionality and anisotropy. Motivated by and capitalizing on this property,contourlet transform can be applied in a wide range of image processing tasks, such asdenoising, and has shown its potential in the field of medical image processing.
The objectives of this dissertation were to introduce contourlet analysistechniques into the ultrasound image processing in HIFU system and make properadaption to clinical applications, including image statistical modeling, ultrasoundspeckle suppressing and treatment target edge detection for real-time HIFU therapy.
Firstly, to further analyse the statistical characteristics of the contourletcoefficients for ultrasound image, we construct a new asymmetric piecewisegeneralized Gaussian function (APGGF) for image statistical modeling in transformdomain. By fitting the statistical probability more precisely and giving thecorresponding parametric estimation, this parametric modeling method has potentialfor many image processing applications, such as image coding, feature extraction,image denoising, and so on.
Then we present a new contourlet-based speckle reduction method for medical ultrasound images. This method gives a scale-adaptive threshold in Bayesianframework based on modeling the subband contourlet coefficients of the ultrasoundimages after logarithmic transform as generalized Gaussian distribution. An adjustedproportional parameter is proposed for adapting the threshold to medical ultrasoundimages in contourlet domain. According to its less computing time, this ultrasoundimage pre-precessing algorithm can satisfy the HIFU intra-surgery requirement.
For more precisely preprocessing the ultrasound images, a new specklesuppression method for medical ultrasound images based on contourlet transform wasproposed. Modeling the speckle with Rayleigh distribution in logarithmicallytransformed ultrasound images, a maximum a posterior (MAP) estimator is appliedfor speckle reduction via manipulating the coefficients in contourlet domain. Asgeneralized Nakagami distribution provides a better model for the statistic of speckledue to its capability and generality, we also proposed another speckle suppressionmethod according to generalized Nakagami distribution model, and further analysiswere then given for several special cases.
A contourlet based edge detection method was then given to extracting theinside-body mark from ultrasound images for phased HIFU intra-surgery targetlocalization. This method extracts the curve structure in images by detecting modulusmaxima in different scale and different directional coutourlet subbands. Thecharacteristic of multiresolution and multidirection, and the low computationcomplexity make the contourlet based edge detection method a choice for phasedHIFU intra-surgery image processing.
Finally, we discussed the affection of the respiration in HIFU therapy system andthe respiration control techniques in image guided radiotherapy. For HIFUintra-surgery target tracking, there are two key factors: the effectiveness ofinside-body mark detection in real time to locating the treatment target; the positionshift of the phased HIFU lesion is also fast enough to make tracking treatmentpossible. According to the characteristics of the image series scanning mode duringthe surgery system, a target localization method is then given in our clinical phasedHIFU therapy system.
引文
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