三维医学图像精准分割算法研究
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摘要
随着计算机技术的发展和医学影像技术的提高,医学图像不仅可以将患者的身体结构投影到二维平面上来观察,而且能重建出体内的三维空间模型,且成像的分辨率越来越高。医学图像分割,作为一种把图像中感兴趣的区域标注出来的方法,是许多基于医学图像的诊断、治疗和分析应用的重要基础步骤。传统的医学图像分割算法大多基于二维图像来研究,应用到三维数据中也是直接对三维数据中的每一个切片来进行二维分割,然后组合成三维目标模型。这种方法的缺点就是没有考虑到图像中与切片垂直方向上的图像信息。然而,直接在三维空间中进行分割所面临的难点主要在于:(1)受一些分割模型的限制,无法将二维平面上的曲线扩展到三维空间中的曲面,如图论中的最短路径问题2)时间效率低以至于不实用。(3)受显示技术的限制,交互方式不直观。
本文在回顾与分析众多医学图像分割方法的基础上,基于上述出发点,研究了一系列基于三维体数据的分割方法。其中包括自动的分割方法和交互式的分割方法。具体来讲,本文的主要工作和创新点如下:
1.一个好的滤波器对分割结果有着重要的影响,因此,本文首先给出了基于自适应加权中值的双边滤波方法。此方法综合了中值滤波和双边滤波的特点,对冲击噪声和高斯噪声同时具有抑制作用。
2.提出了自动从头部MRI T1数据中分割大脑组织的方法。该方法首先用图分割方法来对数据进行粗分割,然后基于marching cubes方法提取此粗分割结果的表面网格,把此网格作为初始表面,利用参数曲面演化方法来对整个大脑进行分割。
3.提出基于嵌入边缘置信度的活动线交互分割方法。此方法考虑了医学图像中感兴趣组织边缘较弱的特征,克服了传统活动线方法需要较多控制点才能把活动线限制在目标边缘上的弱点。实验证明,在达到同样分割精度条件下,本方法使用更少的控制点,这意味着操作者工作效率的提高。
4.提出基于非线性核函数的元胞自动机交互分割方法。该方法有效减少了边缘区域元胞状态的频繁切换,从而加快了算法的收敛速度。
5.深入研究了GPU的体系结构和用于通用计算的方法。提出了基于GPU实现的三维元胞自动机交互分割方法。实验结果显示,该方法GPU实现的运行效率是CPU版本的90倍左右。
本文最后对全文进行了总结,并对未来的工作进行了展望。
As the development of computer techniques and medical imaging modalities, medical images can not only show the 2D projection of the patient body, but also the 3D volume. Meanwhile, the resolution of the imaging is becoming higher and higher. Medical image segmentation, as an important preprocessing step for many image based medical applications, such as computer aided diagnosis and computer aided treatment, is a technique that separates the medical image into several regions; and each region is satisfied by a predefined criteria. Unfortunately, most of the previous research on the medical image segmentation focused themselves on the 2D images. Even these method are applied on the 3D images, they usually use a slice by slice manner. One major weak point for the slice by slice techniques is that they cannot incorporate the information along the Z direction. However, applying the algorithms on the 3D medical volume is difficult due to the following reasons: (1) It is difficult to extent the curve on 2D image to surface on the 3D volume, due to the limitation of some techniques. For instance, there is no counterpart in 3D (surface) of the curves in 2D for the shortest distance path algorithm in graph theory. (2) Some 3D segmentation methods are so slow that are not suitable to use in practice. (3) It is not convenient to interactive with the volume due to the limitation of the visualization technique.
After reviewing many popular segmentations methods, we proposed several 3D automatic and interactive segmentation schemes. Specifically, the main contributions of this work are as follows:
1. Proposed a weighted center median based bilateral fitter, which is a combination of the median filter and bilateral filter. The proposed filter is able to surpass the impulse noise and Gaussian noise simultaneously.
2. Proposed a hybrid scheme which can extract brain tissue from head MRI T1 scans. Given the rough segmentation by graph cut method, marching cubes based isosruface extraction is performed to extract the rough brain surface, which evolved to the exact brain surface governed by the parameterized deformable model.
3. Proposed embedded edge confidence based live-wire segmentation method. This interactive method considered the weak but sharp edges, which are common presented in medical images. Segmentation results indicated that the proposed method required less control points compared with the conventional live-wire method, which means increased working efficiency of the operator.
4. Proposed nonlinear kernel based interactive segmentation method using cellular automata. The proposed method increased the convergence speed and reduced the status switch time for the cells on the boundary compared with the classical method.
5. By analyzing the architecture and generous propose computing method of the GPU, we proposed a GPU implementation of the 3D cellular automata based interactive segmentation method. Experimental results shown the 90 times faster compared with the CPU implementation.
Finally, the conclusion of this dissertation and the prospect of the research are given.
本文在回顾与分析众多医学图像分割方法的基础上,基于上述出发点,研究了一系列基于三维体数据的分割方法。其中包括自动的分割方法和交互式的分割方法。具体来讲,本文的主要工作和创新点如下:
1.一个好的滤波器对分割结果有着重要的影响,因此,本文首先给出了基于自适应加权中值的双边滤波方法。此方法综合了中值滤波和双边滤波的特点,对冲击噪声和高斯噪声同时具有抑制作用。
2.提出了自动从头部MRI T1数据中分割大脑组织的方法。该方法首先用图分割方法来对数据进行粗分割,然后基于marching cubes方法提取此粗分割结果的表面网格,把此网格作为初始表面,利用参数曲面演化方法来对整个大脑进行分割。
3.提出基于嵌入边缘置信度的活动线交互分割方法。此方法考虑了医学图像中感兴趣组织边缘较弱的特征,克服了传统活动线方法需要较多控制点才能把活动线限制在目标边缘上的弱点。实验证明,在达到同样分割精度条件下,本方法使用更少的控制点,这意味着操作者工作效率的提高。
4.提出基于非线性核函数的元胞自动机交互分割方法。该方法有效减少了边缘区域元胞状态的频繁切换,从而加快了算法的收敛速度。
5.深入研究了GPU的体系结构和用于通用计算的方法。提出了基于GPU实现的三维元胞自动机交互分割方法。实验结果显示,该方法GPU实现的运行效率是CPU版本的90倍左右。
本文最后对全文进行了总结,并对未来的工作进行了展望。
As the development of computer techniques and medical imaging modalities, medical images can not only show the 2D projection of the patient body, but also the 3D volume. Meanwhile, the resolution of the imaging is becoming higher and higher. Medical image segmentation, as an important preprocessing step for many image based medical applications, such as computer aided diagnosis and computer aided treatment, is a technique that separates the medical image into several regions; and each region is satisfied by a predefined criteria. Unfortunately, most of the previous research on the medical image segmentation focused themselves on the 2D images. Even these method are applied on the 3D images, they usually use a slice by slice manner. One major weak point for the slice by slice techniques is that they cannot incorporate the information along the Z direction. However, applying the algorithms on the 3D medical volume is difficult due to the following reasons: (1) It is difficult to extent the curve on 2D image to surface on the 3D volume, due to the limitation of some techniques. For instance, there is no counterpart in 3D (surface) of the curves in 2D for the shortest distance path algorithm in graph theory. (2) Some 3D segmentation methods are so slow that are not suitable to use in practice. (3) It is not convenient to interactive with the volume due to the limitation of the visualization technique.
After reviewing many popular segmentations methods, we proposed several 3D automatic and interactive segmentation schemes. Specifically, the main contributions of this work are as follows:
1. Proposed a weighted center median based bilateral fitter, which is a combination of the median filter and bilateral filter. The proposed filter is able to surpass the impulse noise and Gaussian noise simultaneously.
2. Proposed a hybrid scheme which can extract brain tissue from head MRI T1 scans. Given the rough segmentation by graph cut method, marching cubes based isosruface extraction is performed to extract the rough brain surface, which evolved to the exact brain surface governed by the parameterized deformable model.
3. Proposed embedded edge confidence based live-wire segmentation method. This interactive method considered the weak but sharp edges, which are common presented in medical images. Segmentation results indicated that the proposed method required less control points compared with the conventional live-wire method, which means increased working efficiency of the operator.
4. Proposed nonlinear kernel based interactive segmentation method using cellular automata. The proposed method increased the convergence speed and reduced the status switch time for the cells on the boundary compared with the classical method.
5. By analyzing the architecture and generous propose computing method of the GPU, we proposed a GPU implementation of the 3D cellular automata based interactive segmentation method. Experimental results shown the 90 times faster compared with the CPU implementation.
Finally, the conclusion of this dissertation and the prospect of the research are given.
引文
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