Luyin Zhao

Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD

In this paper, we propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule CAD. It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (66 true nodules and 123 false ones) acquired by multi-slice CT scans. From 23 features calculated for each detected structure, the suggested method determined 9 as the optimal feature subset size and selected the nine features. A support vector machine-based classifier trained with the optimal feature subset has resulted in 92.4% sensitivity and 85.4% specificity using leave-one-out cross validation. Experiments also showed significant improvement achieved by a system incorporating the proposed method over a system without it. It can be also applied to other machine learning problems: e.g. computer-aided diagnosis of lung nodules.

Feature subset selection for improving the performance of false positive reduction in lung nodule CAD

We propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule computer-aided detection (CAD). It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set, and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (52 true nodules and 443 false ones) acquired by multislice CT scans. From 23 features calculated for each detected structure, the suggested method determined ten to be the optimal feature subset size, and selected the most relevant ten features. A support vector machine classifier trained with the optimal feature subset resulted in 100% sensitivity and 56.4% specificity using an independent validation set. Experiments show significant improvement achieved by a system incorporating the proposed method over a system without it. This approach can be also applied to other machine learning problems; e.g. computer-aided diagnosis of lung nodules

Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset

One challenge facing radiologists is the characterization of whether a pulmonary nodule detected in a CT scan is likely to be benign or malignant. We have developed an image processing and machine learning based computer-aided diagnosis (CADx) method to support such decisions by estimating the likelihood of malignancy of pulmonary nodules. The system computes 192 image features which are combined with patient age to comprise the feature pool. We constructed an ensemble of 1000 linear discriminant classifiers using 1000 feature subsets selected from the feature pool using a random subspace method. The classifiers were trained on a dataset of 125 pulmonary nodules. The individual classifier results were combined using a majority voting method to form an ensemble estimate of the likelihood of malignancy. Validation was performed on nodules in the Lung Imaging Database Consortium (LIDC) dataset for which radiologist interpretations were available. We performed calibration to reduce the differences in the internal operating points and spacing between radiologist rating and the CADx algorithm. Comparing radiologists with the CADx in assigning nodules into four malignancy categories, fair agreement was observed (κ=0.381) while binary rating yielded an agreement of (κ=0.475), suggesting that CADx can be a promising second reader in a clinical setting.