基于仿滤子的CT图像特征选择对非小细胞肺癌的鉴别

Differential diagnosis of non-small cell lung cancer by CT image feature selection based on imitating filter

肺癌是世界上最致命的疾病之一。不同类型肺癌的CT图像分类也是计算机辅助诊断中最重要的问题之一。由于可以从计算机断层图像中提取许多图像特征,并且一些特征与最终诊断结果无关,因此该方法仍然是一项困难的任务。在本研究中,我们提出了一种基于仿虑子的方法来产生最佳特征集,并最小化输出特征的无关性。所提出的特征选择策略不仅可以生成最优的特征子集,而且可以在指定的参数设置下约束不相关特征的误发现率。针对所提出的特征选择策略确定的特征,对支持向量机分类器进行训练,得到曲线下面积为(0.86±0.02)。实验结果表明,该方法对肺癌的诊断具有潜在价值。

Lung cancer is one of the deadliest diseases worldwide and the classification of different types of lung cancers in computed tomography(CT)images is also one of the most significant issues in computer-aided diagnosis.It remains a tough task since various image features could be extracted from one single image while part of the features is irrelevant to the final diagnosis results.In this study,a knockoff filter-based approach is proposed to produce the optimal feature set and to minimise the irrelevancy of the output features for the classification of lung cancer in CT images.The proposed feature selection strategy not only can generate the optimal feature subset but also constrain the false discovery rate of the irrelevant features under a specified parameter setting.Ten-fold leave-one-out cross-validation and the area under the receiver operating characteristic curve are both adopted in the experiments to evaluate the performance of the proposed method.The areas under curve of(0.86±0.02)is achieved when the support vector machine classifier is trained on the features determined by the proposed feature selection strategy.The experimental results demonstrate that the presented approach is potentially valuable for lung cancer diagnosis.