基于肺部CT序列图像的肺实质三维分割

目的:肺实质分割是基于CT图像的肺结节计算机辅助检测技术必不可少的步骤。结合阈值技术、连通区域标记以及形态学技术,提出了一种简单有效的从CT图像中分割三维肺实质的方法,以期能为后续肺结节计算机辅助检测技术的研究奠定基础。方法:首先,将原图像二值化,并应用三维连通域标记去除背景及细小空洞;然后,经三维区域生长法去除气管;最后,经形态学滤波平滑肺边界得到肺部精确的三维模板,并采用该模板从CT序列图像中分割出肺实质。结果:根据对20组层厚2.0mm、每组约250个切片的肺部CT临床数据实验验证,其肺实质分割的平均正确度为91.55%,处理单组数据平均耗时167.4563s。结论:实验结果表明,本文方法能自动快速地从CT序列图像中分割出肺实质。     

CT图像肺结节自动检测

肺癌是对人类生命健康危害最大的恶性肿瘤之一。计算机辅助诊断系统对肺部CT图像进行自动分析后,可提示医生可疑肺结节,从而克服医生在诊断中的一些主观因素,为此本文提出了一种基于胸部CT图像的可疑肺结节自动检测算法。首先,根据胸部组织的特殊结构,利用一种新的分割算法提取出肺实质部分;在此基础上提取出灰度与结节相近的感兴趣区域,包括结节、肺血管、支气管;然后,以已标记的结节数据作为样本集,计算结节的面积、灰度均值、灰度方差、圆形度、形状矩、体积、球形度等特征值,利用最近邻法建立分类器判别函数;最后,计算测试集感兴趣区域的上述特征,对其进行判别、分类,并标记出结节。试验结果表明,该算法综合考虑了肺结节特征,具有较高的准确度。     

基于重采样的胸部CT图像肺实质自动分割

目的胸部CT图像的肺实质自动分割是肺部疾病计算机辅助检测的重要基础。为提高分割速度,本文提出并实现了一种基于重采样的分割算法。方法首先对数据重采样,提取部分（1／8）体数据。再基于重采样体数据,通过阈值分割、胸腔提取、气管剔除、血管填充、左右肺分离和肺壁结节填充等步骤,得到初步分割结果。然后将该结果还原到完整数据体上,形态学平滑后即完成最终分割。最后将算法应用于20例患者数据（2556个断层）,并与放射科医生手动分割结果进行比较。结果本文算法对20例患者数据均能取得优异结果,与放射科医生手动分割的平均面积重叠率达99．02％,且适用于左右肺相连、肺壁存在结节、视野不完整等异常情况。通过数据重采样极大缩短分割时间,一般可缩短50％,一帧图像平均耗时小于0．25s。结论本文算法能够实现胸部cT图像肺实质的自动分割,结果准确可靠,鲁棒性好,速度快,基本满足实际临床需求。     

CT图像肺结节计算机辅助检测技术研究进展

根据近年来国内外计算机辅助检测(computer-aided detection,CAD)技术在CT图像肺结节检测中的研究进展情况,本文对比分析了目前检测流程中6个阶段(图像采集、预处理、肺实质分割、感兴趣区域提取、特征提取与优化、检测分析与降低假阳性率)各自所运用的研究方法及问题,并提出肺结节检测准确率的提高,依赖于各步骤算法的优化和大样本标准病例数据库的建立,需要在研究针对单一类型结节分类算法的基础上,设计通用的结节分类算法。     

基于CT图像的肺结节计算机辅助检测技术的研究进展

目的:通过对国内外在基于CT图像的肺结节计算机辅助检测技术(Computer-Aided Detection,CAD)领域的研究状况及其研究进展的综述,以期能对CAD研究提供借鉴.方法:根据对近年来国内外报道的CAD文献的分析和深入研究,结合作者在CAD方面的研究体会,本文对CAD国内外研究进展情况、存在的问题及可能的解决方案进行了详细的论述.结果:通过对目前国内外CAD研究中存在的问题及原因的深入分析,我们认为在建立能够描述不同类型肺结节的数学模型的基础上,基于大样本病历的数据库,设计有效的检测方法是提高肺结节检测效率的关键.结论:CAD能有效辅助放射科医生从CT图像中检测出肺结节,从而为肺癌的早期诊断奠定基础,但由于CAD研究尚存在的局限性,目前报道的CAD研究离临床的实际需要尚有较大的差距.     

基于3DV-Net的肺结节检测分割算法

目的：提出基于深度学习的肺结节识别与分割算法,以辅助医生进行肺部疾病检测。方法：针对LUNA16数据集数据量大以及肺结节种类大小多样性等特征,采用基于改进的深度神经网络3DV-Net实现多种肺结节的检测分割,然后使用ResNet对结节图像和非结节图像进行分类。对LUNA16数据集中的肺部CT图像进行图像去噪、插值采样等预处理,然后生成粗分割图像和Mask图像,再使用改进的3DV-Net模型对数据进行多次训练预测。网络层级越深,出现梯度消散、梯度爆炸等问题的概率越大,改进的3DV-Net使用残差连接来改善这一问题。结果：改进的3DV-Net的Dice相似系数和IoU分别达到88.29%和88.25%。结论：本文方法有助于肺结节的检测分割,在肺结节的辅助诊断方面有重要意义。     

基于胸部CT图像的肺结节分割

目的提出一种从胸部CT图像中分割提取多种类型肺结节的算法,辅助肺癌诊断和疗效评估。方法首先由放射科医生确定种子点和目标容积区域,再根据初分割结果自动识别非肺壁粘连结节和肺壁粘连结节。然后采用多阈值结合距离变换的方法分割非肺壁粘连结节,光线投射和直线拟合分割肺壁粘连结节。最后,将算法应用于85组患者数据（232个肺结节）,并由高年资放射科医生评价分割结果的准确性。结果本文算法鲁棒性强,能准确判别肺壁粘连和非肺壁粘连结节,从而适用于孤立、血管粘连、毛玻璃和肺壁粘连结节的提取。测试的232个结节中无异常发生,且分割速度较快。经放射医生评价,平均准确率达90％。结论本文算法可以从胸部CT图像中分割提取4种类型肺结节,鲁棒性、准确性和速度均可满足实际临床需求,对肺癌筛查、诊断和疗效评估具有重要价值。     

基于CT图像的肺结节计算机辅助诊断系统

本文介绍了一种基于CT图像的肺结节计算机辅助自动诊断系统。我们将肺结节的自动检测分为肺实质的提取、感兴趣区域（ROI）的分割和ROI特征参数提取及分类判别几个步骤。该系统能够在对肺部CT图像进行自动分析后给医生提示出可疑肺结节，从而提高了医疗诊断效率。     

基于改进凸包算法的肺实质分割研究

肺实质的精确分割一直都是肺部疾病计算机辅助诊断的重要研究内容,传统的分割方法大多只能分割出不包含病灶的肺实质区域,为后期的图像分析与辅助决策带来很大的影响。针对具有边缘型肺结节的肺部CT图像,提出一种实现简单且实验效果较好的肺实质分割算法。首先,利用常规方法提取肺实质的粗略轮廓;然后,针对上一步骤中肺实质病灶信息等的缺失现象,提出一种改进的二维凸包算法对肺实质的外轮廓进行再修复;最后,利用区域生长和形态学运算,修复肺实质的内部轮廓。运用新算法,对200张边缘型肺结节的肺部CT图像进肺实质分割。实验结果表明：与已有的“滚球法”和凸包算法修复肺实质相比,新算法具有较高的准确率,可以达到90%以上,边缘型肺结节等病灶信息能被较为准确地表示出来,为建立高效的肺部疾病诊断系统奠定基础。     

肺部CT图像气管树分割技术研究进展

肺部气管是人体与外界进行气体交换的唯一通路;其解剖结构信息可用于诊断呼吸系统疾病。计算机断层扫描技术(CT)是检测呼吸系统疾病的主要手段,但因就诊人数多、图像数据量大等因素;导致人工阅片费时费力。而肺部气管树的自动提取与分割;是实现自动化定量分析与呼吸系统疾病辅助诊断的前提。首先对肺部气管树分割技术的背景及意义进行介绍;然后分析对比传统分割技术、基于管状结构检测的分割技术以及基于机器学习的分割技术所运用的研究方法和存在的问题。最后指出提高肺部气管树分割效果;依赖于将气管分割技术与泄漏剔除技术相互结合;需要在尽可能分割出多数气管树分枝的基础上;消除分割结果中存在的伪气管区域。     

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient?=?98.62 and shape differentiation metrics d_mean?=?1.39 mm, and d_rms?=?2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.     

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.     

A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.     

CT结肠造影中息肉的自动识别

目的：通过计算机自动识别CT结肠造影中结肠息肉的方法，提高结肠息肉筛查的灵敏度及效率。方法：利用计算CT图像的偏导数得到等值面的几何形态，找到符合息肉特征的体素，通过区域生长及模糊分类完成对息肉的识别与分割，并将识别到的息肉用特殊颜色标记，在其引导下进行虚拟内窥镜重建。结果：自动识别方法对息肉的总体敏感度为58．8％，假阳性率为4．7个，病例，平均检查时间为15．3min。同人工方法相比，检查时间平均缩短40．5％，且对5-10mm息肉的敏感度有明显提高（P=0．046）。结论：本方法具有较高的识别灵敏度，能加快息肉筛查的速度，可作为结肠息肉筛查的辅助手段。     

A Fully Automatic Method for Lung Parenchyma Segmentation and Repairing

Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.     

The Normal Mode Analysis Shape Detection Method for Automated Shape Determination of Lung Nodules

Surface morphology and shape in general are important predictors for the behavior of solid-type lung nodules detected on CT. More broadly, shape analysis is useful in many areas of computer-aided diagnosis and essentially all scientific and engineering disciplines. Automated methods for shape detection have all previously, to the author’s knowledge, relied on some sort of geometric measure. I introduce Normal Mode Analysis Shape Detection (NMA-SD), an approach that measures shape indirectly via the motion it would undergo if one imagined the shape to be a pseudomolecule. NMA-SD allows users to visualize internal movements in the imaging object and thereby develop an intuition for which motions are important, and which geometric features give rise to them. This can guide the identification of appropriate classification features to distinguish among classes of interest. I employ normal mode analysis (NMA) to animate pseudomolecules representing simulated lung nodules. Doing so, I am able to assign a testing set of nodules into the classes circular, elliptical, and irregular with roughly 97 % accuracy. This represents a proof-of-principle that one can obtain shape information by treating voxels as pseudoatoms in a pseudomolecule, and analyzing the pseudomolecule’s predicted motion.