无线胶囊内窥镜图像处理技术研究
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摘要
消化道疾病,如出血、肿瘤和溃疡等极大地威胁着人体的健康。传统的消化道检查工具如X射线、钡餐和X射线断层扫描等都是利用间接成像原理,会对人体造成不同程度的损伤。超声检测虽然对人体没有任何损害,但是其分辨能力极为有限。插入式胃镜和直肠镜可以对人体消化道进行直接观测,利用光纤对影像进行传输,但是这些工具会对人体带来不适,同时由于小肠的长度限制,传统的插入式内镜均不能对整个小肠段进行检查。新型的双气囊小肠镜可以帮助医生对病人的小肠全段进行检查,但长时间的检查过程同样会对人体带来很大的不适。
无线胶囊内窥镜(Wireless Capsule Endoscopy,WCE),简称无线内镜或者胶囊内镜,是一种新型的无创式胃肠道检查工具。这种普通胶囊大小的内窥镜由微小的元器件组成,当病人吞服后,内窥镜开始以2帧每秒的速度在病人消化道内拍摄图像,并将拍摄到的图像经无线传输方式传送到病人体外的接收终端,最终由医生利用图像工作站对图像进行诊断。与传统的X射线断层扫描、磁共振成像以及插入式内窥镜相比,该技术可以无创的检测整个小肠。无线内镜的发展揭开了小肠检查技术的新篇章。根据以色列基文公司的统计,目前已经进行了超过一百万例(?)PillCam无线内镜检查帮助医生评估病人的胃肠道状态。基于无线内镜的胃肠道检查,临床医生能够早期发现包括小肠肿瘤在内的各种严重胃肠道疾病。目前无线内镜检查是公认的小肠肿瘤检查的金标准。
尽管无线内镜检查技术为小肠疾病的诊断开辟了一个全新的领域,但是目前仍然存在一个明显的挑战需要解决。无线内镜视频图像数量巨大而且部分图像质量不佳,对无线内镜视频的判读是一个漫长而枯燥的过程。根据临床的操作规程,这个过程至少需要两位经验丰富的临床医生花费大约45分钟到2个小时的时间。因此,研究无线内镜视频及图像的计算机辅助检查方法,减轻医生的工作负担是十分必要的。围绕这一目标,本论文主要致力于解决无线胶囊内窥镜计算机辅助诊断系统设计中的一些具有挑战性的问题。为了对无线内镜视频进行计算机辅助诊断,提高无线内镜检查的准确性,本文从数字图像处理技术的三个不同层次,即图像处理、图像分析和图像识别,对无线内镜图像进行了研究,探讨了数字图像处理技术中的相关理论和方法在无线内镜视频计算机辅助诊断系统中的应用。
从图像处理方面,我们提出了一种基于偏微分方程的新型图像增强方法,将图像对比度增强,图像去噪及边缘锐化技术集成到一个偏微分方程模型中,三种操作同时进行得到最终的增强结果。在图像演化过程中,三种不同的增强操作同时进行,利用正则化参数控制三种操作对最终结果的影响,直至得到最满意的结果。基于人工合成图像、自然图像和无线内镜彩色图像的实验结果表明,与传统的图像增强方法相比,本文提出的偏微分方程模型可以获得更加令人满意的增强效果,从而起到辅助医生诊断的作用。
随后我们利用图像分析的方法对无线内镜图像进行处理,研究了无线内镜视频摘要技术,可以使医生不必浏览全部视频就可以得到视频的主要信息。提出了基于“感兴趣场景”的镜头检测技术,建立了无线内镜视频静态故事板。在静态故事板的基础上,在医生的监督下,建立了无线内镜视频动态故事板。实验结果表明,该方法能够提取视频中最具代表意义的视频帧,约减了需目视检查的图像数量,降低了医生的工作负担。
从图像理解方面,本文致力于研究出血以及肿瘤这两种常见小肠疾病的计算机自动诊断技术。首先我们提出了无线内镜出血图像检测方法,可以更为有效的检测图像中的出血区域。由于图像中的边缘像素和出血像素具有相似的色度,传统算法不能对二者进行正确区分。我们提出了一种图像预处理方法,检测图像中的边缘像素点并利用形态学方法进行去除。然后提出了一种超像素分割方法,将图像根据颜色和纹理分割成图像块,在图像块的水平上进行出血检测,降低了算法的复杂度。最后提出了一种新型的颜色特征用以区分出血与非出血像素并使用支持向量机对超像素进行分类。实验结果表明该方案能够有效地诊断出血图像同时大大降低了运算代价。其次,针对肿瘤图像,我们提出了一个新的肿瘤图像检测技术框架。为提高检测准确率,首先对图像中的高光反射区域进行去除。利用均值漂移技术对原始图像进行粗分割。粗分割后的图像首先进行出血检测,提取带出血特征的肿瘤图像。使用自适应阈值分割算法对出血检测后的图像进行处理,提取图像中的疑似肿瘤区域,利用几何特征分析方法对图像进行最终检测,提取非出血肿瘤图像。实验结果表明与传统方法相比,本文方法可以有效地提取视频中的各种肿瘤图像。
总体而言,我们在论文中研究了无线内镜视频计算机辅助诊断系统中诸如图像增强、视频摘要、病症自动诊断等具有挑战性的问题,同时系统的提出了各种问题的解决方案,大量的实验结果以及医生的评价证明了我们所提出方案的有效性。
Diseases of the Gastrointestinal (GI) tract, such as bleeding, tumor and ulcer, are great threats to human health. The traditional imaging modalities, such as X-ray, barium, CT and double-balloon enteroscopy are invasive to human body. Ultrasound modality, though no invasiveness to body, but suffer from low resolution in images. The gastroscopy and colonoscopy, which make use of fiber optics for light and video transmission, are not only do they cause discomfort, but also these devices fail to diagnose diseases in the small intestine since this region is out of reach by these devices. Double-balloon enteroscopy allows for visualization of the entire small intestine to the terminal ileum, but this procedure require that patients be admitted to hospital for exceed three hours.
Wireless Capsule Endoscopy (WCE) is a state-of-the art technology that en-ables imaging of the entire human gastrointestinal tract without invasiveness. It is a pill-shaped device which consists of some miniature components. After swallowed by the patient, it takes pictures of the GI tract and transmits images out of the human body wirelessly. Compared to the traditional methods such as gastroscopy and CT, this new technique can view the entire small intestine without pain, sedation or X-ray radiation. The development of WCE has opened a new chapter in small intestine examination. More than one million PillCam video capsules have helped clinicians evaluate patients for GI disorders since Given Imaging Ltd. produced WCE. Based on WCE of the GI tract, clinicians are now able to detect severe diseases include tumor in early development states. It is recognized as the gold standard method for examining small intestine and make early tumor diagnosis possible.
Despite of many significant breakthroughs, there exist one major problem con-cerning the WCE images. The problem is that the viewing process of the video data for the physicians per examination is very time consuming because of the low con-trast image quality and great amount of the video data. This task can only be carried out by two trained clinicians. The duration of this assessment typically varies from45minutes to two hours. If we could use computerized methods to help the physi-cians detect some abnormal regions in the WCE images, it will of course reduce the burden of the physicians. Concentrated on this goal, this dissertation mainly studies some main challenging problems in the development of computer aided diagnosis system for WCE images.
To aid clinicians and improve the accuracy of endoscopy capsule endoscopy video diagnosis, we research on WCE images processing methods from three differ-ent levels of digital image processing technology:image processing, image analysis and image recognition, explore the feasibility of a variety of image processing tech-niques used in the WCE computer-aided diagnosis system.
For image processing, a novel approach for image enhancement is presented in this thesis. The enhancement result is achieved by combination the means of contrast enhancement, image smoothing and image sharpening techniques into one partial differential equation (PDE) framework. In the image evolution process, three kinds of operation execute at the same time, using the regularization parameters to adjust the proportion of each operation in the evolution process, ultimately achieve the desired results. The simulations for both gray, color and WCE images are given in the experiments. The experimental results show that the proposed framework can simultaneously perform contrast enhancement, image sharpening and smoothing op-eration. Compared with the traditional sequential processing methods, the proposed PDE based framework can achieve better visual effect so as to assist both the inspec-tion and the computer aided detection.
Afterwards, we investigate the image analysis methods for the WCE images. First, a mechanism that allows the clinicians to gain certain evaluation of a video without watching the whole video is designed. A shot detection based method is presented for automatically establishing the WCE video static storyboard, and then moving storyboard is extracted based on the selected representative frames under the supervision of clinicians. Experimental results show that most of the representative frames containing relevant features can be extracted from the original WCE video. The proposed method can significantly and safely reduce the number of frames that need to be examined by clinicians and thus speed up the diagnosis procedures.
For image recognition, the computer aided diagnosis (CAD) system is designed. In section5, we propose a new method which can detect bleeding regions from WCE video more effectively and efficiently. Since edge pixels and bleeding pixels share similar hue, traditional algorithms often mistake edge pixels for bleeding pixels. We first detect the edge pixels, and then use the morphological dilation to locate and remove the edge regions. Instead of processing each pixel or dividing the image uniformly, we group pixels adaptively based on color and location through super-pixel segmentation. Thus each image can be represented by hundreds of superpix-els and the computational complexity is also reasonable. For each superpixel, the feature is defined using the red ratio in RGB color space. Finally, support vector machine (SVM) is performed to classify the bleeding and non-bleeding superpixels. Experimental results show that the proposed method has low computational com-plexity and maintains high performance as pixel based method. In section6, a novel computer-aided method for tumor detection in WCE video is proposed. In order to improve the detection accuracy, the specular reflectance regions are first removed from WCE image, and mean shift is employed for the initial segmentation of the image. Since tumor may be bleeding or non-bleeding, two schemes are proposed to detect them. After initial segmentation, a red ratio based bleeding detection method is first employed to find the bleeding tumors. Then, an adaptive thresholding algo-rithm is proposed to find the suspected non-bleeding tumor segments. Finally, the true non-bleeding tumor region is picked from them based on geometric analysis. Comparative experiments show that the proposed algorithm is superior to the exist-ing methods in terms of sensitivity and specificity.
In conclusion, this thesis investigates some main challenging problems such as WCE images enhancement, video abstraction and computer aided diagnosis, and some novel methods are proposed to solve those problems successfully. Comparative experiments confirm the effectiveness of our proposed methods.
无线胶囊内窥镜(Wireless Capsule Endoscopy,WCE),简称无线内镜或者胶囊内镜,是一种新型的无创式胃肠道检查工具。这种普通胶囊大小的内窥镜由微小的元器件组成,当病人吞服后,内窥镜开始以2帧每秒的速度在病人消化道内拍摄图像,并将拍摄到的图像经无线传输方式传送到病人体外的接收终端,最终由医生利用图像工作站对图像进行诊断。与传统的X射线断层扫描、磁共振成像以及插入式内窥镜相比,该技术可以无创的检测整个小肠。无线内镜的发展揭开了小肠检查技术的新篇章。根据以色列基文公司的统计,目前已经进行了超过一百万例(?)PillCam无线内镜检查帮助医生评估病人的胃肠道状态。基于无线内镜的胃肠道检查,临床医生能够早期发现包括小肠肿瘤在内的各种严重胃肠道疾病。目前无线内镜检查是公认的小肠肿瘤检查的金标准。
尽管无线内镜检查技术为小肠疾病的诊断开辟了一个全新的领域,但是目前仍然存在一个明显的挑战需要解决。无线内镜视频图像数量巨大而且部分图像质量不佳,对无线内镜视频的判读是一个漫长而枯燥的过程。根据临床的操作规程,这个过程至少需要两位经验丰富的临床医生花费大约45分钟到2个小时的时间。因此,研究无线内镜视频及图像的计算机辅助检查方法,减轻医生的工作负担是十分必要的。围绕这一目标,本论文主要致力于解决无线胶囊内窥镜计算机辅助诊断系统设计中的一些具有挑战性的问题。为了对无线内镜视频进行计算机辅助诊断,提高无线内镜检查的准确性,本文从数字图像处理技术的三个不同层次,即图像处理、图像分析和图像识别,对无线内镜图像进行了研究,探讨了数字图像处理技术中的相关理论和方法在无线内镜视频计算机辅助诊断系统中的应用。
从图像处理方面,我们提出了一种基于偏微分方程的新型图像增强方法,将图像对比度增强,图像去噪及边缘锐化技术集成到一个偏微分方程模型中,三种操作同时进行得到最终的增强结果。在图像演化过程中,三种不同的增强操作同时进行,利用正则化参数控制三种操作对最终结果的影响,直至得到最满意的结果。基于人工合成图像、自然图像和无线内镜彩色图像的实验结果表明,与传统的图像增强方法相比,本文提出的偏微分方程模型可以获得更加令人满意的增强效果,从而起到辅助医生诊断的作用。
随后我们利用图像分析的方法对无线内镜图像进行处理,研究了无线内镜视频摘要技术,可以使医生不必浏览全部视频就可以得到视频的主要信息。提出了基于“感兴趣场景”的镜头检测技术,建立了无线内镜视频静态故事板。在静态故事板的基础上,在医生的监督下,建立了无线内镜视频动态故事板。实验结果表明,该方法能够提取视频中最具代表意义的视频帧,约减了需目视检查的图像数量,降低了医生的工作负担。
从图像理解方面,本文致力于研究出血以及肿瘤这两种常见小肠疾病的计算机自动诊断技术。首先我们提出了无线内镜出血图像检测方法,可以更为有效的检测图像中的出血区域。由于图像中的边缘像素和出血像素具有相似的色度,传统算法不能对二者进行正确区分。我们提出了一种图像预处理方法,检测图像中的边缘像素点并利用形态学方法进行去除。然后提出了一种超像素分割方法,将图像根据颜色和纹理分割成图像块,在图像块的水平上进行出血检测,降低了算法的复杂度。最后提出了一种新型的颜色特征用以区分出血与非出血像素并使用支持向量机对超像素进行分类。实验结果表明该方案能够有效地诊断出血图像同时大大降低了运算代价。其次,针对肿瘤图像,我们提出了一个新的肿瘤图像检测技术框架。为提高检测准确率,首先对图像中的高光反射区域进行去除。利用均值漂移技术对原始图像进行粗分割。粗分割后的图像首先进行出血检测,提取带出血特征的肿瘤图像。使用自适应阈值分割算法对出血检测后的图像进行处理,提取图像中的疑似肿瘤区域,利用几何特征分析方法对图像进行最终检测,提取非出血肿瘤图像。实验结果表明与传统方法相比,本文方法可以有效地提取视频中的各种肿瘤图像。
总体而言,我们在论文中研究了无线内镜视频计算机辅助诊断系统中诸如图像增强、视频摘要、病症自动诊断等具有挑战性的问题,同时系统的提出了各种问题的解决方案,大量的实验结果以及医生的评价证明了我们所提出方案的有效性。
Diseases of the Gastrointestinal (GI) tract, such as bleeding, tumor and ulcer, are great threats to human health. The traditional imaging modalities, such as X-ray, barium, CT and double-balloon enteroscopy are invasive to human body. Ultrasound modality, though no invasiveness to body, but suffer from low resolution in images. The gastroscopy and colonoscopy, which make use of fiber optics for light and video transmission, are not only do they cause discomfort, but also these devices fail to diagnose diseases in the small intestine since this region is out of reach by these devices. Double-balloon enteroscopy allows for visualization of the entire small intestine to the terminal ileum, but this procedure require that patients be admitted to hospital for exceed three hours.
Wireless Capsule Endoscopy (WCE) is a state-of-the art technology that en-ables imaging of the entire human gastrointestinal tract without invasiveness. It is a pill-shaped device which consists of some miniature components. After swallowed by the patient, it takes pictures of the GI tract and transmits images out of the human body wirelessly. Compared to the traditional methods such as gastroscopy and CT, this new technique can view the entire small intestine without pain, sedation or X-ray radiation. The development of WCE has opened a new chapter in small intestine examination. More than one million PillCam video capsules have helped clinicians evaluate patients for GI disorders since Given Imaging Ltd. produced WCE. Based on WCE of the GI tract, clinicians are now able to detect severe diseases include tumor in early development states. It is recognized as the gold standard method for examining small intestine and make early tumor diagnosis possible.
Despite of many significant breakthroughs, there exist one major problem con-cerning the WCE images. The problem is that the viewing process of the video data for the physicians per examination is very time consuming because of the low con-trast image quality and great amount of the video data. This task can only be carried out by two trained clinicians. The duration of this assessment typically varies from45minutes to two hours. If we could use computerized methods to help the physi-cians detect some abnormal regions in the WCE images, it will of course reduce the burden of the physicians. Concentrated on this goal, this dissertation mainly studies some main challenging problems in the development of computer aided diagnosis system for WCE images.
To aid clinicians and improve the accuracy of endoscopy capsule endoscopy video diagnosis, we research on WCE images processing methods from three differ-ent levels of digital image processing technology:image processing, image analysis and image recognition, explore the feasibility of a variety of image processing tech-niques used in the WCE computer-aided diagnosis system.
For image processing, a novel approach for image enhancement is presented in this thesis. The enhancement result is achieved by combination the means of contrast enhancement, image smoothing and image sharpening techniques into one partial differential equation (PDE) framework. In the image evolution process, three kinds of operation execute at the same time, using the regularization parameters to adjust the proportion of each operation in the evolution process, ultimately achieve the desired results. The simulations for both gray, color and WCE images are given in the experiments. The experimental results show that the proposed framework can simultaneously perform contrast enhancement, image sharpening and smoothing op-eration. Compared with the traditional sequential processing methods, the proposed PDE based framework can achieve better visual effect so as to assist both the inspec-tion and the computer aided detection.
Afterwards, we investigate the image analysis methods for the WCE images. First, a mechanism that allows the clinicians to gain certain evaluation of a video without watching the whole video is designed. A shot detection based method is presented for automatically establishing the WCE video static storyboard, and then moving storyboard is extracted based on the selected representative frames under the supervision of clinicians. Experimental results show that most of the representative frames containing relevant features can be extracted from the original WCE video. The proposed method can significantly and safely reduce the number of frames that need to be examined by clinicians and thus speed up the diagnosis procedures.
For image recognition, the computer aided diagnosis (CAD) system is designed. In section5, we propose a new method which can detect bleeding regions from WCE video more effectively and efficiently. Since edge pixels and bleeding pixels share similar hue, traditional algorithms often mistake edge pixels for bleeding pixels. We first detect the edge pixels, and then use the morphological dilation to locate and remove the edge regions. Instead of processing each pixel or dividing the image uniformly, we group pixels adaptively based on color and location through super-pixel segmentation. Thus each image can be represented by hundreds of superpix-els and the computational complexity is also reasonable. For each superpixel, the feature is defined using the red ratio in RGB color space. Finally, support vector machine (SVM) is performed to classify the bleeding and non-bleeding superpixels. Experimental results show that the proposed method has low computational com-plexity and maintains high performance as pixel based method. In section6, a novel computer-aided method for tumor detection in WCE video is proposed. In order to improve the detection accuracy, the specular reflectance regions are first removed from WCE image, and mean shift is employed for the initial segmentation of the image. Since tumor may be bleeding or non-bleeding, two schemes are proposed to detect them. After initial segmentation, a red ratio based bleeding detection method is first employed to find the bleeding tumors. Then, an adaptive thresholding algo-rithm is proposed to find the suspected non-bleeding tumor segments. Finally, the true non-bleeding tumor region is picked from them based on geometric analysis. Comparative experiments show that the proposed algorithm is superior to the exist-ing methods in terms of sensitivity and specificity.
In conclusion, this thesis investigates some main challenging problems such as WCE images enhancement, video abstraction and computer aided diagnosis, and some novel methods are proposed to solve those problems successfully. Comparative experiments confirm the effectiveness of our proposed methods.
引文
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