Abstract
To avoid the deformation of spicules surrounding pulmonary nodules caused by the classic rubber band straightening transform (RBST), we propose a novel RBST technique to extract spicules-based features. In this paper, the run-length statistics (RLS) features are extracted from the RBST image, in which a smooth circumference with a suitable radius inside the nodule is proposed as the border of transformed object. An experimental sample set of 814 images of pulmonary nodules was used to verify the proposed feature extraction technique. The best accuracy, sensitivity and specificity achieved based on the proposed features were 79.4%, 66.5%, 89.2%, respectively, and the area under the receiver operating characteristic curve was 87.0%. These results indicate that the proposed method of feature extraction is promising for classifying benign and malignant pulmonary nodules.
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Acknowledgments
This work was partially funded by the National Natural Science Foundation of China under Grant (No. 60972122) and the Natural Science Foundation of Shanghai under Grant (No. 14ZR1427900).
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He, X., Gong, J., Wang, L., Nie, S. (2017). Extraction Technique of Spicules-Based Features for the Classification of Pulmonary Nodules on Computed Tomography. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_13
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DOI: https://doi.org/10.1007/978-981-10-6370-1_13
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