Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric-Reference-Cited by-同舟云学术

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Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric

Published:2017-11-29 Issue:1 Volume:42 Page:
ISSN:0148-5598
Container-title:Journal of Medical Systems
language:en
Short-container-title:J Med Syst

Author:

Wei Guohui,Cao Hui,Ma He,Qi Shouliang,Qian Wei,Ma Zhiqing

Funder

the Recruitment Program of Global Experts

the National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Link

http://link.springer.com/article/10.1007/s10916-017-0874-5/fulltext.html

Reference22 articles.

1. Siegel, R.L., Miller, K.D., and Jemal, A., Cancer statistics, 2015. CA Cancer J Clin. 65:5–29, 2015. https://doi.org/10.3322/caac.21254 .

2. Armato, R.S., Mclennan, G., Bidaut, L., et al., The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 38(2):915–931, 2011. https://doi.org/10.1118/1.3528204 .

3. Huang, P., Park, S., Yan, R., et al., Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology. 5:162725, 2017. https://doi.org/10.1148/radiol.2017162725 .

4. Lu, C., Zhu, Z., and Gu, X., An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method. J Med Syst. 38(9):97–105, 2014. https://doi.org/10.1007/s10916-014-0097-y .

5. Orozco, H., Villegas, O., Sánchez, V., Domínguez, H., and Alfaro, M., Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. BioMed Eng. 14(1):9, 2015. https://doi.org/10.1186/s12938-015-0003-y.

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