基于纹理特征的颅脑CT图像病变自动化检出算法研究
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摘要
随着医学影像技术的飞速发展，包括多排螺旋CT在内的先进检查设备产生的影像数据清晰度越来越高，容量也越来越大。海量的容积数据在为影像诊断医师提供更加详细、更加准确的诊断信息的同时，也显着增加了读片医师的工作负担和视觉疲劳。传统的人工读片方式与越来越先进的检查设备之间的矛盾随着这些设备的不断普及应用而变得日益突出。为了适应医学影像检查技术的飞速发展，开展以计算机辅助诊断或计算机智能化诊断为目标的医学图像处理和分析研究已经成为目前这个领域的一个研究热点和将来发展的主要趋势。
开展计算机辅助诊断和智能化诊断研究首先必须解决的问题是如何实现医学图像上病变的计算机自动化检出。病变的自动化检出又必须涉及到图像配准，图像分割，数字化图谱创建等多项基本的图像处理和分析技术。本文瞄准计算机智能化诊断这个方向，以多排螺旋CT扫描的颅脑CT图像为研究对象，以颅脑CT图像的纹理信息为研究切入点，以颅脑病变的自动化检出为研究目标，通过密切结合医学专业知识，对病变检出及其相关技术进行了深入的研究，取得了多项具有创新意义的研究成果。具体成果和创新点介绍如下：
1．提出了以颅脑CT图像的纹理特征作为病变自动化检出的依据，研究并构造了一种基于树结构小波变换的纹理层析向量，用于描述颅脑CT图像特征解剖部位的纹理特征。本文在研究各种病变的计算机自动化检出算法的基础上，发现目前的病变检出算法很少重视图像的纹理信息。纹理描述了图像象素间的相互依赖关系；小波变换作为多尺度、多通道的分析工具，为纹理信息在不同尺度上的分析和表征提供了精确、统一的框架，应用小波变换算法构造的纹理层析向量可以有效的描述图像的纹理信息。据此本文研究并构造了一种基于树结构小波变换的纹理层析向量。实验结果表明，这种纹理层析向量鲁棒性强，且具有唯一性、平移不变性，并在一定范围内满足旋转不变性，可以有效的描述图像的纹理信息，为本论文后面的研究工作奠定了基础。
2．针对基于特征的非刚性配准算法，提出了一种基于纹理层析向量的对应标志点的自动搜索算法。非刚性配准技术是实现病变检出的核心，本文在深入研究各类非刚性配准技术的基础上，针对基于特征的非刚性配准算法需要手工介入，无法实现完全自动化的缺点，提出了一种基于纹理层析向量的对应标志点搜索算法。本文通过在待配准的颅脑CT图像内自动搜索与目标图像具有相似纹理区域的标志点，实现了对应标志点的全自动搜索。实验结果证明，这种对应标志点的自动搜索算法标记正确率较高，可以解决基于特征的非刚性配准算法需要手工介入的问题。
3．提出了一种新型的数字化统计图谱——纹理层析向量图谱，并实现了颅脑CT图像纹理层析向量图谱的计算机自动化创建。正常图像主要是通过数字化统计图谱进行计算机描述，本文在研究了现有类型的数字化统计图谱后，为弥补现有类型图谱在描述正常人图像纹理特征方面的不足，利用前面有关纹理层析向量的研究结果，提出了基于纹理层析向量的颅脑CT图像的数字化统计图谱的构造算法。本文成功的实现了二维颅脑CT图像的纹理层析向量图谱的计算机自动化创建。纹理层析向量图谱描述了颅脑CT图像的纹理特征。
4．应用颅脑CT图像的纹理层析向量图谱，实现了对钙化、脑出血等病变的计算机自动化检出。由于大多数病变的影像学表现为图像的纹理变化，因此本文从分析图像的纹理信息入手，利用前面创建的颅脑CT图像的纹理层析向量图谱，通过比较待诊断图像各个区域的纹理层析向量与图谱间的差异，实现了对钙化、脑出血等病变的计算机自动化检出。
With the rapid development of science and technology, new and modern equipments such as Multi-slice Spiral Computed Tomography have become the leading instruments for medical imaging. These equipments produce volume data of high quality and large quantity. While volume data provides more and more detailed and accurate diagnostic information for the radiologist, it also increases significantly the workload of radiologists. Overload would exacerbate the radiologist's fatigue and increase the possibility of overlooking and mistaking in the course of film reading. Instead of making diagnosis easier,new equipment puts radiologists into a dilemma and its advantages have not been exploited. As these increasingly advanced equipments are rapidly and widely put into use, the incompatibility between modern equipments and traditional human film reading would become even more evident and more acute. To keep pace with the development of modern medical imaging technology, research directing to realize Computer Aided Diagnosis or Computer Intelligent Diagnosis has become the major challenge and general trend in the field of medical imaging processing and analysis.
The first and biggest obstacle to clear on the way to computer intelligent diagnosis is how to detect the lesion on the medical image. In order to successfully detect pathology on medical image, many of the image processing techniques, such as registration, segmentation and construction of digital probabilistic atlas have to be studied. With the direction of future research on Computer Intelligent Diagnosis, with the goal of pathology automatic detection based on the texture information on the cerebral Computed Tomography, we heve made a in-depth study on the method of pathology automatic detection and its related technique by combining domain knowledge of medical image, and we have put forward a few of innovative algorithm. The major contributions of the dissertation are as follows:
1. A method which carries out computer-automatic diagnosis according as the texture feature of the cerebral CT image was put forward, and a kind of Texture Layers-Analysis Vector based on tree-structured wavelet transform which characterizes the texture feature of the cerebral CT image was studied and constructed in this paper. After researching the computer-automatic diagnosis algorithms, the texture information of image was attached importance to, which was ignored in the other algorithms. Because the texture information characterizes the relationship between the pixels in the image, and the wavelet transform offers the analysis and the expressing of the texture feature a kind of exact and uniform explanation, the Texture Layers-Analysis Vector is able to characterize the texture information of the cerebral CT image exactly. Experiment results showed that the texture layers-analysis vector is robust, unique in the image, and it is invariable after shifting and rotating in a range. The research of the Texture Layers-Analysis Vector facilitates the work in this paper later.
2. An algorithm of automatic detection to corresponding points based on the texture layers-analysis vector was proposed in cerebral CT images. NRR is the key step to pathology detection, therefore a variety of algorithms of NRR was first reviewed and introduced in this paper. A algorithm of automatic detection to corresponding points based on the texture layers-analysis vector was put forward later because the existing NRR algorithm is not automatic completely. In this algorithm the point which has similar texture information is pick out as the corresponding point. Experiment results showed that the correct rate of this algorithm is satisfying.
3. The digital probabilistic atlas based on texture layers-analysis texture in 2D cerebral CT image was constructed automatically. The digital probabilistic atlases were reviewed in this paper. We proposed the digital probabilistic atlas algorithm based on the texture layers-analysis vector according to the results before because the other atlases have a shortage of characterizing the normal feature of cerebral CT image. In this paper, the digital probabilistic atlas based on the texture layers-analysis vector was constructed automatically.
4. Using the digital probabilistic atlas based on the texture layers-analysis vector, we detected calcify, cerebral hemorrhage, etc automatically with computer. Because most representation of the pathology is the texture changes, according to comparing the difference between the texture layers-analysis vectors which characterizes the texture information of the cerebral CT image, a variety of pathological changes such as calcify, cerebral hemorrhage was detected automatically.
开展计算机辅助诊断和智能化诊断研究首先必须解决的问题是如何实现医学图像上病变的计算机自动化检出。病变的自动化检出又必须涉及到图像配准，图像分割，数字化图谱创建等多项基本的图像处理和分析技术。本文瞄准计算机智能化诊断这个方向，以多排螺旋CT扫描的颅脑CT图像为研究对象，以颅脑CT图像的纹理信息为研究切入点，以颅脑病变的自动化检出为研究目标，通过密切结合医学专业知识，对病变检出及其相关技术进行了深入的研究，取得了多项具有创新意义的研究成果。具体成果和创新点介绍如下：
1．提出了以颅脑CT图像的纹理特征作为病变自动化检出的依据，研究并构造了一种基于树结构小波变换的纹理层析向量，用于描述颅脑CT图像特征解剖部位的纹理特征。本文在研究各种病变的计算机自动化检出算法的基础上，发现目前的病变检出算法很少重视图像的纹理信息。纹理描述了图像象素间的相互依赖关系；小波变换作为多尺度、多通道的分析工具，为纹理信息在不同尺度上的分析和表征提供了精确、统一的框架，应用小波变换算法构造的纹理层析向量可以有效的描述图像的纹理信息。据此本文研究并构造了一种基于树结构小波变换的纹理层析向量。实验结果表明，这种纹理层析向量鲁棒性强，且具有唯一性、平移不变性，并在一定范围内满足旋转不变性，可以有效的描述图像的纹理信息，为本论文后面的研究工作奠定了基础。
2．针对基于特征的非刚性配准算法，提出了一种基于纹理层析向量的对应标志点的自动搜索算法。非刚性配准技术是实现病变检出的核心，本文在深入研究各类非刚性配准技术的基础上，针对基于特征的非刚性配准算法需要手工介入，无法实现完全自动化的缺点，提出了一种基于纹理层析向量的对应标志点搜索算法。本文通过在待配准的颅脑CT图像内自动搜索与目标图像具有相似纹理区域的标志点，实现了对应标志点的全自动搜索。实验结果证明，这种对应标志点的自动搜索算法标记正确率较高，可以解决基于特征的非刚性配准算法需要手工介入的问题。
3．提出了一种新型的数字化统计图谱——纹理层析向量图谱，并实现了颅脑CT图像纹理层析向量图谱的计算机自动化创建。正常图像主要是通过数字化统计图谱进行计算机描述，本文在研究了现有类型的数字化统计图谱后，为弥补现有类型图谱在描述正常人图像纹理特征方面的不足，利用前面有关纹理层析向量的研究结果，提出了基于纹理层析向量的颅脑CT图像的数字化统计图谱的构造算法。本文成功的实现了二维颅脑CT图像的纹理层析向量图谱的计算机自动化创建。纹理层析向量图谱描述了颅脑CT图像的纹理特征。
4．应用颅脑CT图像的纹理层析向量图谱，实现了对钙化、脑出血等病变的计算机自动化检出。由于大多数病变的影像学表现为图像的纹理变化，因此本文从分析图像的纹理信息入手，利用前面创建的颅脑CT图像的纹理层析向量图谱，通过比较待诊断图像各个区域的纹理层析向量与图谱间的差异，实现了对钙化、脑出血等病变的计算机自动化检出。
With the rapid development of science and technology, new and modern equipments such as Multi-slice Spiral Computed Tomography have become the leading instruments for medical imaging. These equipments produce volume data of high quality and large quantity. While volume data provides more and more detailed and accurate diagnostic information for the radiologist, it also increases significantly the workload of radiologists. Overload would exacerbate the radiologist's fatigue and increase the possibility of overlooking and mistaking in the course of film reading. Instead of making diagnosis easier,new equipment puts radiologists into a dilemma and its advantages have not been exploited. As these increasingly advanced equipments are rapidly and widely put into use, the incompatibility between modern equipments and traditional human film reading would become even more evident and more acute. To keep pace with the development of modern medical imaging technology, research directing to realize Computer Aided Diagnosis or Computer Intelligent Diagnosis has become the major challenge and general trend in the field of medical imaging processing and analysis.
The first and biggest obstacle to clear on the way to computer intelligent diagnosis is how to detect the lesion on the medical image. In order to successfully detect pathology on medical image, many of the image processing techniques, such as registration, segmentation and construction of digital probabilistic atlas have to be studied. With the direction of future research on Computer Intelligent Diagnosis, with the goal of pathology automatic detection based on the texture information on the cerebral Computed Tomography, we heve made a in-depth study on the method of pathology automatic detection and its related technique by combining domain knowledge of medical image, and we have put forward a few of innovative algorithm. The major contributions of the dissertation are as follows:
1. A method which carries out computer-automatic diagnosis according as the texture feature of the cerebral CT image was put forward, and a kind of Texture Layers-Analysis Vector based on tree-structured wavelet transform which characterizes the texture feature of the cerebral CT image was studied and constructed in this paper. After researching the computer-automatic diagnosis algorithms, the texture information of image was attached importance to, which was ignored in the other algorithms. Because the texture information characterizes the relationship between the pixels in the image, and the wavelet transform offers the analysis and the expressing of the texture feature a kind of exact and uniform explanation, the Texture Layers-Analysis Vector is able to characterize the texture information of the cerebral CT image exactly. Experiment results showed that the texture layers-analysis vector is robust, unique in the image, and it is invariable after shifting and rotating in a range. The research of the Texture Layers-Analysis Vector facilitates the work in this paper later.
2. An algorithm of automatic detection to corresponding points based on the texture layers-analysis vector was proposed in cerebral CT images. NRR is the key step to pathology detection, therefore a variety of algorithms of NRR was first reviewed and introduced in this paper. A algorithm of automatic detection to corresponding points based on the texture layers-analysis vector was put forward later because the existing NRR algorithm is not automatic completely. In this algorithm the point which has similar texture information is pick out as the corresponding point. Experiment results showed that the correct rate of this algorithm is satisfying.
3. The digital probabilistic atlas based on texture layers-analysis texture in 2D cerebral CT image was constructed automatically. The digital probabilistic atlases were reviewed in this paper. We proposed the digital probabilistic atlas algorithm based on the texture layers-analysis vector according to the results before because the other atlases have a shortage of characterizing the normal feature of cerebral CT image. In this paper, the digital probabilistic atlas based on the texture layers-analysis vector was constructed automatically.
4. Using the digital probabilistic atlas based on the texture layers-analysis vector, we detected calcify, cerebral hemorrhage, etc automatically with computer. Because most representation of the pathology is the texture changes, according to comparing the difference between the texture layers-analysis vectors which characterizes the texture information of the cerebral CT image, a variety of pathological changes such as calcify, cerebral hemorrhage was detected automatically.
引文
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