Abstract
To improve the classification performance for lung nodule, we proposed a lung nodule CT image feature extraction method. The approach designed multi-directional distribution features to represent nodules in different risk stages effectively. First, the reference map is constructed using integral image, and then K-Means approach is performed to clustering the reference map and calculate its label map. The density distribution map of lung nodule image was generated after calculate the gray scale density distribution level for each pixel. An exponential function was designed to weighting the angular histogram for each components of the distribution map. Then, quantitative measurement was performed by Random Forest classifier. The evaluation dataset is the lung CT database which provided by Shanghai Zhongshan Hospital (ZSDB), the nodule risk categories were AAH, AIS, MIA, and IA. In the result the AUCs are 0.9771, 0.9917, 0.9590, 0.9971, and accuracy are 0.7478, 0.9167, 0.7450, 0.9567 for AAH, AIS, MIA and IA respectively. The experiments show that the proposed method performs well and is effective to improve the classification performance of pulmonary nodules.
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References
Emil, J.B., Neil, M.R., Ryan, Z.: Appendicitis: the impact of computed tomography imaging on negative appendectomy and perforation rates. Am. J. Gastroenterol. 93, 768–771 (1998)
Goo, J.M., Park, C.M., Lee, H.J.: Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma. Am. J. Roentgenol. 196, 533–543 (2011)
Noriyuki, S., Hidetake, Y., Masatoshi, K., et al.: Volumetric measurement of artificial pure ground-glass nodules at low-dose CT: comparisons between hybrid iterative reconstruction and filtered back projection. Eur. J. Radiol. 84, 2654–2662 (2015)
El-Baz, A., Nitzken, M., Khalifa, F., et al.: 3D shape analysis for early diagnosis of malignant lung nodules. In: Biennial International Conference on Information Processing in Medical Imaging, pp. 772–783 (2011)
Jacobs, C., van Rikxoort, E.M., Twellmann, T., et al.: Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med. Image Anal. 18, 374–384 (2014)
Maldonado, F., Duan, F., Raghunath, S.M., et al.: Noninvasive computed tomography–based risk stratification of lung adenocarcinomas in the National Lung Screening Trial. Am. J. Respir. Crit. Care Med. 192, 737–744 (2015)
Zhu, Y., Tan, Y., Hua, Y., et al.: Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. J. Digit. Imaging 23, 51–65 (2010)
Li, W., Cao, P., Zhao, D., Wang, J.: Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput. Math. Methods Med. (2016)
Setio, A.A.A., Ciompi, F., Litjens, G., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35, 1160–1169 (2016)
Son, J.Y., Lee, H.Y., Lee, K.S., et al.: Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS ONE 9, e104066 (2014)
Foley, F., Rajagopalan, S., Raghunath, S.M., et al.: Computer-aided nodule assessment and risk yield risk management of adenocarcinoma: the future of imaging? Semin. Thorac. Cardiovasc. Surg. 28, 120–126 (2016)
Yeh, Y.-C., Kadota, K., Nitadori, J.-I., et al.: International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification predicts occult lymph node metastasis in clinically mediastinal node-negative lung adenocarcinoma. Eur. J. Cardiothorac. Surg. 49, 9–15 (2016)
Bose, S., Pal, A., SahaRay, R., Nayak, J.: Generalized quadratic discriminant analysis. Pattern Recogn. 48, 2676–2684 (2015)
Acknowledgements
The authors greatly appreciate the financial supported by Zhongshan Hospital Clinical Research Foundation No. 2016ZSLC05, No. 2016ZSCX02, National Key Scientific and Technology Support Program No. 2013BAI09B09 and Natural Science Foundation of China No. 81500078.
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Le, V., Zhu, Y., Yang, D., Zheng, B., Ren, X. (2018). A Quantitative Analysis System of Pulmonary Nodules CT Image for Lung Cancer Risk Classification. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_2
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DOI: https://doi.org/10.1007/978-981-10-8108-8_2
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