Computerized lung cancer malignancy level analysis using 3D texture features

Wenqing Sun; Xia Huang; Tzu-Liang Tseng; Jianying Zhang; Wei Qian

doi:10.1117/12.2216329

24 March 2016 Computerized lung cancer malignancy level analysis using 3D texture features

Wenqing Sun, Xia Huang, Tzu-Liang Tseng, Jianying Zhang, Wei Qian

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978538 (2016) https://doi.org/10.1117/12.2216329
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Based on the likelihood of malignancy, the nodules are classified into five different levels in Lung Image Database Consortium (LIDC) database. In this study, we tested the possibility of using threedimensional (3D) texture features to identify the malignancy level of each nodule. Five groups of features were implemented and tested on 172 nodules with confident malignancy levels from four radiologists. These five feature groups are: grey level co-occurrence matrix (GLCM) features, local binary pattern (LBP) features, scale-invariant feature transform (SIFT) features, steerable features, and wavelet features. Because of the high dimensionality of our proposed features, multidimensional scaling (MDS) was used for dimension reduction. RUSBoost was applied for our extracted features for classification, due to its advantages in handling imbalanced dataset. Each group of features and the final combined features were used to classify nodules highly suspicious for cancer (level 5) and moderately suspicious (level 4). The results showed that the area under the curve (AUC) and accuracy are 0.7659 and 0.8365 when using the finalized features. These features were also tested on differentiating benign and malignant cases, and the reported AUC and accuracy were 0.8901 and 0.9353.

Citation Download Citation

Wenqing Sun, Xia Huang, Tzu-Liang Tseng, Jianying Zhang, and Wei Qian "Computerized lung cancer malignancy level analysis using 3D texture features", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978538 (24 March 2016); https://doi.org/10.1117/12.2216329

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