共查询到16条相似文献,搜索用时 187 毫秒
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针对肺部CT图像因各组织灰度不均匀、结构复杂等因素造成双肺边界难以准确分割的问题,提出了一种多阈值和标记分水岭相融合的肺部分割方法。首先采用多阈值法对肺部CT图像进行粗分割,并去除图像中气管与主支气管;然后采用标记控制分水岭方法进行精分割,并利用形态学运算对肺实质边缘修补,最后采用临床肺CT图像在Matlab 2012平台上对算法性能进行仿真测试。结果表明,本文方法可以较好保留肺部CT图像的边界信息,提高了肺部CT图像分割精度,误分和错分概率大幅度下降,取得了十分理想的分割结果,为肺部疾病临床医学诊断提供了有价值的参考信息。 相似文献
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基于CT图像的肺实质分割不仅仅是后续图像处理最基础和最重要的技术,而且是一个典型的亟待解决的问题。本文利用水平集方法能初步获取较好的目标轮廓的特点和分水岭算法准确的边缘检测能力,提出一种基于水平集和分水岭相结合的改进轮廓检测算法。该算法采用由粗糙到准确的方式,在运用水平集演化初步检测目标轮廓的基础上,进一步运用标记分水岭算法检测准确的轮廓边界。结果表明,该方法能实现肺实质分割,解决了肺结节检测的预处理问题。 相似文献
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针对肺结节分割中存在的自动化程度低、较少考虑空间结构以及粘附型肺结节分割不充分问题,提出了一种基于空间分布的三维自动化肺结节分割算法.该算法首先利用C-means聚类算法分割出肺实质,然后根据肺结节空间分布的差异性将其分为3类:孤立性肺结节、胸膜粘附性肺结节、血管粘附性肺结节,并对3种不同类型的肺结节分别采用基于连通性、灰度下降和散度差异的分割算法进行分割,70个肺结节(其中孤立性肺结节38个,血管粘附性肺结节17个,胸膜粘附性肺结节15个)CT图像的实验结果表明,算法能够准确、自动地分割出3种不同部位的肺结节. 相似文献
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在基于多阈值的脑,CT图像分割算法中,最佳阈值选取是脑CT图像中的关键,针对传统多阈值法的阈值选择难题为了提高脑。CT图像的分割准确率,提出一种萤火虫群算法优化多阈值的脑CT图像分割方法首先建立了基于多阈值法的脑图像分割数学模型,然后通过萤火虫群算法数学模型进行求解,搜索到脑CT图像分割的最佳阈值,CT最后采用最佳阈值完成脑CT图像的分割。仿真结果表明,萤火虫群算法提高了脑CT图像的精度,获得了更加理想的脑CT图像结果。 相似文献
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介绍了一种新的CR胸片肺实质分割算法。在对CR胸片肺实质分割的目的、背景进行简单的介绍后,给出了基于OTSU和局部阈值的二次肺实质分割算法的设计思想及具体实现过程;并采用此算法对大量数据进行了测试,最后给出测试结果并进行总结。 相似文献
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肺实质分割结果的准确性在实际临床应用中具有非常重要的意义。但由于肺结节的位置、大小、形状的不规则性,肺部病变的多样性,以及人体胸部解剖结构的明显差异等,使得各类分割方法不能统一地适用于所有的胸部CT图像,所以对于肺实质分割方法的研究仍具有很大的挑战。该文在国内外研究分析的基础上提出基于3D区域增长法与改进的凸包修补算法相结合的全肺分割方法。在3D区域增长法的粗分割基础上,对分割的结果进行细化工作,通过连通域标记法与形态学方法相结合去除气管和主支气管,得到初步的肺实质掩膜,最后应用改进的凸包算法对肺部轮廓进行修补平滑,最终得到肺部分割结果。通过与凸包算法及滚球法相对比,证明该文所提改进的凸包算法能够有效地修补肺部轮廓凹陷,修补后的结果分割精度较高。 相似文献
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边界模糊图像不同区域之间没有明确的分界,用传统的图像分割方法难以得到很好的分割结果。本文研究了径向基函数网络的工作实质及其用于图像分割的机理,分析了径向基函数神经网络的特点,针对边界模糊图像,应用不同结构的径向基函数神经网络对其进行图像分割,验证了径向基函数网络用于图像分割的有效性以及算法速度上的优越性。 相似文献
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新型冠状病毒肺炎肆虐全球,严重影响了人类社会的生活和健康。CT影像技术是检测新冠肺炎的重要诊断方式,从CT图像中自动准确分割出新冠肺炎病灶区域,对于诊断、治疗和预后都有重要意义。针对新冠肺炎病灶的自动分割,文中提出基于Inf-Net算法改进的自动分割方法,通过引入通道注意力机制加强特征表示,并运用注意力门模块来更好地融合边缘信息。在COVID-19 CT分割数据集上的实验结果表明,文中所提出新冠肺炎图像分割方法的Dice系数、灵敏度、特异率分别为75.1%、75.4%和95.4%,算法性能也优于部分主流方法。 相似文献
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Rule-based detection of intrathoracic airway trees 总被引:2,自引:0,他引:2
New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection. 相似文献
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Tschirren J Hoffman EA McLennan G Sonka M 《IEEE transactions on medical imaging》2005,24(12):1529-1539
The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations. 相似文献