Fully automatic detection of lung nodules in CT images using a hybrid featureset
Fully automatic detection of lung nodules in CT images using a hybrid featureset
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DOI：
10.1002/mp.12273
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发表时间：
2017-07-01
期刊：
影响因子：
3.8
通讯作者：
Frangi, Alejandro F.
中科院分区：
文献类型：
--
作者：
Shaukat, Furqan;Raja, Gulistan;Frangi, Alejandro F.
本研究的目的是利用优化的特征集开发一种新的肺结节检测技术。该特征集是通过严格的实验获得的，这有助于显著降低误报。方法该方法首先进行预处理，去除输入图像中存在的任何噪声，然后使用最优阈值分割肺部。然后，在结节检测和特征提取之前，使用多尺度点增强滤波对图像进行增强。最后，利用支持向量机分类器对肺结节进行分类。特征集由强度、形状(2D和3D)和纹理特征组成，这些特征被用来优化敏感度和减少误报。除了支持向量机，其他一些有监督的分类器，如K近邻(KNN)、决策树和线性判别分析(LDA)也被用于性能比较。还对提取的特征进行了分类比较，以确定与肺结节检测最相关的特征。使用LIDC(LIDC)数据集的850次扫描和k重交叉验证方案对该系统进行了评估。结果与以前的方法相比，该系统的总体灵敏度得到了提高，并且每次扫描的误检率显著降低。在检测和分类阶段获得的灵敏度分别为94.20%和98.15%，每次扫描仅有2.19个假阳性。结论仅使用单一的特征类很难获得高性能的度量，因此混合方法仍然是较好的选择。选择合适的特征集可以通过提高敏感度和减少误报来提高系统的整体准确率。(C)2017年美国医学物理学家协会
PurposeThe aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly.MethodThe proposed method starts with preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected tooptimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K-Nearest-Neighbor (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class-wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k-fold cross-validation scheme.ResultsThe overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 false positives per scan.ConclusionsIt is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives. (C) 2017 American Association of Physicists in Medicine