A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models.
A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models.
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基于多尺度滤波和统计模型的多模态血管造影血管分割方法
DOI：
10.1186/s12938-016-0241-7
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发表时间：
2016-11-08
影响因子：
3.9
通讯作者：
Wang C
中科院分区：
文献类型：
--
作者：
Lu P;Xia J;Li Z;Xiong J;Yang J;Zhou S;Wang L;Chen M;Wang C
准确的血管分割在血管疾病的计算机辅助诊断和介入治疗中起着重要的作用。统计方法是有效血管分割的重要组成部分；然而，有几个限制阻碍了分割效果，即对图像形态的依赖、不均匀的对比剂、偏场以及对象和背景的重叠强度分布。此外，根据图像直方图的特点，建立了统计方法的混合模型。因此，如何利用传统的方法从多模式血管造影图像中进行血管分割是一个具有挑战性的问题。为了克服这些局限性，人们提出了一种灵活的分割方法，该方法具有固定的混合模型，适用于各种血管造影模式。我们的方法主要由三部分组成。首先，对原始图像进行多尺度滤波，增强血管，抑制噪声。因此，过滤后的数据获得了新的统计特征。其次，建立了三种概率分布(两个指数分布和一个高斯分布)组成的混合模型来拟合滤波数据的直方图曲线，其中使用期望最大化(EM)算法进行参数估计。最后，利用三维马尔可夫随机场(MRF)来提高像素分类和后验概率估计的精度。为了定量评估该方法的性能，设计了两个具有不同管状结构和噪声的模拟血管的模型。同时，使用来自不同人体器官的四个临床血管造影数据集对该方法进行了定性验证。为了进一步测试该方法的性能，在两个不同的脑磁共振血管成像(MRA)数据集上进行了与传统方法的对比测试。模型的实验结果令人满意，噪声得到了很好的抑制，误分类体素的百分比(即分割错误率)不超过0.3%，Dice相似系数(DSC)在94%以上。根据临床血管专家的意见，由于提取了完整的血管树，所以各种数据集中的血管提取精度很高，而较少的非血管和背景被错误地归类为血管。对比实验表明，该方法对复杂背景噪声下的多模式血管造影图像的血管结构提取具有较高的准确性和较强的鲁棒性。实验结果表明，该方法适用于各种血管造影数据。这主要是因为所构建的混合概率模型能够从各种血管造影图像的多尺度滤波数据中对血管目标进行统一分类。该方法的优点在于：首先，由于多尺度滤波算法可以在对比剂不均匀和偏场等情况下提高血管强度，因此可以提取出血管成像质量较差的血管；其次，在各种信号噪声的情况下，该方法对多模血管成像图像中的血管提取效果较好；第三，与传统方法相比，该方法具有更高的准确性和鲁棒性。总的来说，这些特点预示着所提出的方法将具有重要的临床应用价值。
Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases. The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i.e., dependence of the image modality, uneven contrast media, bias field, and overlapping intensity distribution of the object and background. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms. Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images. To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities. Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions (two Exponential distributions and one Gaussian distribution) was built to fit the histogram curve of the filtered data, where the expectation maximization (EM) algorithm was used for parameters estimation. Finally, three-dimensional (3D) Markov random field (MRF) were employed to improve the accuracy of pixel-wise classification and posterior probability estimation. To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method. To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography (MRA) data sets. The results of the phantoms were satisfying, e.g., the noise was greatly suppressed, the percentages of the misclassified voxels, i.e., the segmentation error ratios, were no more than 0.3%, and the Dice similarity coefficients (DSCs) were above 94%. According to the opinions of clinical vascular specialists, the vessels in various data sets were extracted with high accuracy since complete vessel trees were extracted while lesser non-vessels and background were falsely classified as vessel. In the comparison experiments, the proposed method showed its superiority in accuracy and robustness for extracting vascular structures from multi-modality angiographic images with complicated background noises. The experimental results demonstrated that our proposed method was available for various angiographic data. The main reason was that the constructed mixture probability model could unitarily classify vessel object from the multi-scale filtered data of various angiography images. The advantages of the proposed method lie in the following aspects: firstly, it can extract the vessels with poor angiography quality, since the multi-scale filtering algorithm can improve the vessel intensity in the circumstance such as uneven contrast media and bias field; secondly, it performed well for extracting the vessels in multi-modality angiographic images despite various signal-noises; and thirdly, it was implemented with better accuracy, and robustness than the traditional methods. Generally, these traits declare that the proposed method would have significant clinical application.
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DOI：
10.1016/j.compmedimag.2013.12.005
发表时间：
2015-07-01
影响因子：
5.7
作者：
Mesejo, Pablo;Valsecchi, Andrea;Damas, Sergio
通讯作者：
Damas, Sergio
影响因子：
10.6
作者：
Wilson, DL;Noble, JA
通讯作者：
Noble, JA
影响因子：
3.8
作者：
Zhou, Chuan;Chan, Heang-Ping;Kazerooni, Ella A.
通讯作者：
Kazerooni, Ella A.
影响因子：
6.1
作者：
Oliveira, Roberta B.;Filho, Mercedes E.;Tavares, Joao Manuel R. S.
通讯作者：
Tavares, Joao Manuel R. S.
影响因子：
10.9
作者：
Hassouna, MS;Farag, AA;Moriarty, T
通讯作者：
Moriarty, T