Department of Electrical Engineering, University of Engineering & Technology, Taxila, 47080, Pakistan.
Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK.
Med Phys. 2017 Jul;44(7):3615-3629. doi: 10.1002/mp.12273. Epub 2017 Jun 16.
The 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.
The 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 to optimize 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.
The 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.
It 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.
本研究旨在开发一种使用优化特征集的新型肺结节检测技术。通过严格的实验，获得了该特征集，这有助于显著减少假阳性。
该方法首先进行预处理，从输入图像中去除任何现有噪声，然后使用最佳阈值进行肺部分割。然后，在进行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，使用支持向量机（SVM）分类器对肺结节进行分类。特征集由强度、形状（二维和三维）和纹理特征组成，这些特征经过选择以优化敏感性并减少假阳性。除了 SVM 之外，还使用了一些其他监督分类器，如 K-最近邻（KNN）、决策树和线性判别分析（LDA），以进行性能比较。还对提取的特征进行了分类比较，以确定最相关的肺结节检测特征。该系统使用来自 Lung Image Database Consortium（LIDC）数据集的 850 次扫描和 k 折交叉验证方案进行了评估。
与以前的方法相比，整体敏感性得到了提高，并且每次扫描的假阳性率显著降低。在检测和分类阶段，灵敏度分别达到 94.20%和 98.15%，每次扫描的假阳性率仅为 2.19。
仅使用单个特征类别很难实现高性能指标，因此特征选择中的混合方法仍然是更好的选择。选择正确的特征集可以通过提高敏感性和减少假阳性来提高系统的整体准确性。
