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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT
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The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT

机译：基于形状分析的特征以及HRCT的纹理分析，可提高阻塞性肺疾病自动分类的性能

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摘要

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16×16, 32×32, and 64×64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16×16, 32×32, 64×64 pixels, respectively (t-test, p ＜ 0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.
机译：在本文中，我们基于HRCT（高分辨率计算机断层扫描）图像提出了新颖的形状特征，以提高区分阻塞性肺疾病的分类性能。这些图像选自HRCT图像，该图像来自82位受试者。对于每张图像，两位经验丰富的放射科医生选择具有各种尺寸（16×16、32×32和64×64像素）的矩形ROI，以代表每种疾病或正常的肺实质。除了13个纹理特征外，我们还采用了另外7个形状特征。群集形状特征和高顶转换特征。为了评估形状特征对阻塞性肺疾病分化的贡献，使用两种不同类型的分类器和各种ROI大小进行了一些实验。对于自动分类，实现了贝叶斯分类器和支持向量机（SVM）。为了评估系统的性能和交叉验证，使用了5折方法。与仅使用纹理特征相比，添加形状特征可显着提高总体灵敏度（在贝叶斯算法中为5.9、5.4、4.4％，在SVM中为9.0、7.3、5.3％），其ROI大小为16×16、32 ×32、64×64像素（t检验，p ＜0.01）。此外，这种增强很大程度上是由于轻度小叶性肺气肿和闭塞性细支气管炎的类别特异性敏感性有所改善，而放射科医生最难区分。根据这些实验结果，在常规贝叶斯分类器和SVM分类器中，将形状特征添加到常规纹理特征对于改善阻塞性肺疾病的分类性能非常有用。

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