医学图像分割与虚拟手术几个关键问题的研究
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摘要
医学影像在现代医疗诊断过程中具有极其重要的作用，许多外科手术的实施、病症的诊断都需要医学影像技术的参与。现代的医疗中对影像技术的应用已经不再仅限于单纯的阅读影像图片等一般性的操作了。人们开始以所获得的图像为基础，融合各种先进技术，从更广泛的角度分析和处理图像，力求挖掘更深层次的信息，为诊断和治疗提供详实可靠依据。在处理和分析医学图像的各种技术中，既包含图像分割、特征提取、三维重建等基础性内容，也包含了计算机辅助诊断，虚拟外科手术，手术导航等实际的应用内容。本论文选取了分割技术中的脑皮层分割、脑肿瘤分割、三维重建中的目标体虚拟移动以及术前设计系统的构建作为研究重点，以构建实用的外科手术术前设计系统作为最终目的。
脑皮层具有非常复杂的结构，它的每一个部分都与人体的一部分的功能相对应。脑皮层分割与表示是进行脑皮层分析、揭示这些功能映射的重要环节。同时脑皮质层分割与表示也是是医学影像领域中的一个难点。本文提出了一种基于水平集的自适应分割模型来实现脑皮质层的分割与表示。该模型是一种自适应混合水平集带状模型。它结合了基于区域分割和基于边界分割算法的优点，以基于区域的水平集模型来描述区域信息，并以此作为水平集模型演化动力；以统计双边缘检测算法来描述边缘信息，并以此促使水平集的演化能停止在合理的位置上。这样整个模型既保持了区域模型的稳健性又保持了边界模型的准确性。由于实际的皮质层具有近似相同厚度，本文中将脑皮层的这一特点作为先验知识融合到分割模型中，使得该模型在表征脑皮层分割上更加合理。此外，模型中还充分考虑了MR图像中可能存在的偏移场因素，在模型中增加了对偏移场的描述，有效的克服了偏移场问题。本文分别选用仿真脑图像数据和真实脑图像数据进行实验，并与其它方法进行比较。实验结果证明，自适应带状模型具有稳健、精确的分割效果。与其它方法相比，具有很大的优势。
Medical Imaging has played an important role in modern medical treatment and diagnosis, many surgical operations and disease diagnosis procedure need the participant of medical imaging technology. The application of medical imaging technology in modern medical treatment has no more limited in common operations like reading the picture and reconstructing the 3D image. In order to dig out deep-seated infomation and provide accurate and reliable foundation for diagnose and curation, people begin to analysis and understand the obtained images more comprehensively by incoorporating many advance technology. The medical image process and analysis technology not only involves many basic contents like the image segmentation, image feature extraction, 3D reconstruction etc, but also include some practical contents like the computer aided diagnose, virtual surgery, surgery guidance etc. This dissertation selects the cortex segmentation, objects motion in 3d reconstruction and presurgery design system as the key research contents, and takes the construction of practical presurgery design system as the ultimate goal.
The cortex has very complex structures, each part of them are correponding to part of human body functions. The cortex segmentation and representation are the important phase to analysis the cortex and unveil the function mappings, meanwhile the cortex segmentation and representation are also the nut to the medical imaging field. In this dissertation, a adaptive ribbon model based on levelsets is proposed to tackle the cortex segmentation and representation problem. This model is naturely a hybrid adaptive levelset model combining both the merits of region and boundary segmentation. The regionbased levelsets are used to represent the region information, and region information act as levelset evolving motivition term. The statistical coupled edges detector is used to describe the boundary information, and the boudary information acts as a stopping term to the evolving process. This model is provided with both the robustness of region model and the preciseness of boundary model. According to the fact that the cortex has the similiar thickness, the model takes this fact as a prior knowledge and shapes into adaptive ribbon model. Besides, the bias field description is added to overcome the influence of the possible bias field in MR image. To demonstrate the proposed model, the simulated brain images and real brain images are used for testing, and comparation to other methods are made. the experiment results show that adaptive ribbon model is more robust and accurate than other models.
Image intensity overlapping between brain tumor and other normal tissues make the brain tumor segmentation a tough thing. For the sake of overcoming this brain tumor segmentation difficulty, a framework based on viscous fluid model registration for brain tumor segmentation is proposed.This framework consists of registration building on viscous fluid
脑皮层具有非常复杂的结构，它的每一个部分都与人体的一部分的功能相对应。脑皮层分割与表示是进行脑皮层分析、揭示这些功能映射的重要环节。同时脑皮质层分割与表示也是是医学影像领域中的一个难点。本文提出了一种基于水平集的自适应分割模型来实现脑皮质层的分割与表示。该模型是一种自适应混合水平集带状模型。它结合了基于区域分割和基于边界分割算法的优点，以基于区域的水平集模型来描述区域信息，并以此作为水平集模型演化动力；以统计双边缘检测算法来描述边缘信息，并以此促使水平集的演化能停止在合理的位置上。这样整个模型既保持了区域模型的稳健性又保持了边界模型的准确性。由于实际的皮质层具有近似相同厚度，本文中将脑皮层的这一特点作为先验知识融合到分割模型中，使得该模型在表征脑皮层分割上更加合理。此外，模型中还充分考虑了MR图像中可能存在的偏移场因素，在模型中增加了对偏移场的描述，有效的克服了偏移场问题。本文分别选用仿真脑图像数据和真实脑图像数据进行实验，并与其它方法进行比较。实验结果证明，自适应带状模型具有稳健、精确的分割效果。与其它方法相比，具有很大的优势。
Medical Imaging has played an important role in modern medical treatment and diagnosis, many surgical operations and disease diagnosis procedure need the participant of medical imaging technology. The application of medical imaging technology in modern medical treatment has no more limited in common operations like reading the picture and reconstructing the 3D image. In order to dig out deep-seated infomation and provide accurate and reliable foundation for diagnose and curation, people begin to analysis and understand the obtained images more comprehensively by incoorporating many advance technology. The medical image process and analysis technology not only involves many basic contents like the image segmentation, image feature extraction, 3D reconstruction etc, but also include some practical contents like the computer aided diagnose, virtual surgery, surgery guidance etc. This dissertation selects the cortex segmentation, objects motion in 3d reconstruction and presurgery design system as the key research contents, and takes the construction of practical presurgery design system as the ultimate goal.
The cortex has very complex structures, each part of them are correponding to part of human body functions. The cortex segmentation and representation are the important phase to analysis the cortex and unveil the function mappings, meanwhile the cortex segmentation and representation are also the nut to the medical imaging field. In this dissertation, a adaptive ribbon model based on levelsets is proposed to tackle the cortex segmentation and representation problem. This model is naturely a hybrid adaptive levelset model combining both the merits of region and boundary segmentation. The regionbased levelsets are used to represent the region information, and region information act as levelset evolving motivition term. The statistical coupled edges detector is used to describe the boundary information, and the boudary information acts as a stopping term to the evolving process. This model is provided with both the robustness of region model and the preciseness of boundary model. According to the fact that the cortex has the similiar thickness, the model takes this fact as a prior knowledge and shapes into adaptive ribbon model. Besides, the bias field description is added to overcome the influence of the possible bias field in MR image. To demonstrate the proposed model, the simulated brain images and real brain images are used for testing, and comparation to other methods are made. the experiment results show that adaptive ribbon model is more robust and accurate than other models.
Image intensity overlapping between brain tumor and other normal tissues make the brain tumor segmentation a tough thing. For the sake of overcoming this brain tumor segmentation difficulty, a framework based on viscous fluid model registration for brain tumor segmentation is proposed.This framework consists of registration building on viscous fluid
引文
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