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Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT

机译：混合重采样和多特征融合可自动识别肺部CT中的腔体影像征象

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摘要

The automatic recognition of cavity imaging signs in lung computed tomography (CT) images is of great importance for early diagnosis and possible cure of lung tuberculosis and cancers. The performance of existing recognition methods which adopt classical technology needs to be improved accordingly. In this paper, we propose an automatic recognition method based on hybrid resampling and multi-feature fusion strategies. The hybrid resampling includes multi-receptive-field and multi-window settings: the former reduces the risk of missing small or large cavities, the latter reserves context information of multiply CT windows more compactly. For multi-feature fusion, we extract features of convolutional neural networks (CNN) and classical methods (histograms of oriented gradients (HOG) and local binary pattern (LBP)). Then we compress CNN-HOG features by principal components analysis (PCA) algorithm and combine them with LBP feature. Finally, we use the fused feature to train a support vector machine (SVM) model for improving classification performance. We evaluate our method on the cavity samples from LIDC-IDRI and LISS publicly available dataset of chest CT scans, which contains 167 cavities in 164 CT images. The experimental results show that fused feature has better discriminative capability than any single feature, and has the highest FS score (0.1472 vs 0.1136) in the group with sensitivity greater than 0.8. The proposed method is compared with the latest methods for the automatic recognition of cavity imaging sign and enables higher sensitivity than the second-best method (85% vs 70%). The experiment shows that the fusion of CNN feature and classical hand-crafted feature makes full use of the complementary information, and improves classification performance when number of samples in our application is limited. (C) 2019 Elsevier B.V. All rights reserved.
机译：肺部计算机断层扫描（CT）图像中腔体成像体征的自动识别对于肺结核和癌症的早期诊断和可能的治愈非常重要。因此，需要改进采用经典技术的现有识别方法的性能。本文提出了一种基于混合重采样和多特征融合策略的自动识别方法。混合重采样包括多接收场和多窗口设置：前者降低了遗漏小腔或大腔的风险，后者更紧凑地保留了多个CT窗口的上下文信息。对于多特征融合，我们提取了卷积神经网络（CNN）和经典方法（定向梯度直方图（HOG）和局部二进制模式（LBP））的特征。然后，通过主成分分析（PCA）算法压缩CNN-HOG特征，并将其与LBP特征相结合。最后，我们使用融合功能来训练支持向量机（SVM）模型以提高分类性能。我们对来自LIDC-IDRI和LISS胸部CT扫描的公开数据集的腔体样本评估了我们的方法，该数据集包含164个CT图像中的167个腔体。实验结果表明，融合特征具有比任何单个特征更好的判别能力，并且在敏感性大于0.8的组中具有最高的FS评分（0.1472对0.1136）。将该方法与最新的腔体影像征象自动识别方法进行了比较，并且比第二好的方法具有更高的灵敏度（85％对70％）。实验表明，CNN特征和经典手工特征的融合充分利用了互补信息，在我们的样本数量有限的情况下提高了分类性能。 （C）2019 Elsevier B.V.保留所有权利。

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