Abstract
A novel computer-aided method based on magnetic resonance images (MRI) was proposed for the early detection and diagnosis of nasopharyngeal carcinoma (NPC). A local Chan-Vese level-set model, which integrated the maximum interclass-variance method with the Chan-Vese model, was built to detect foci with unobvious boundaries. For each of the suspected foci, 26 features, including suspected focus texture, shape, and grayscale characteristics, were extracted, and then classified with a support-vector-machine (SVM) classifier. The method was tested with 289 brain images of 48 patients with nasopharyngeal carcinoma and 33 healthy adults, which obtained an average successful-diagnosis rate of 90.74%.
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Acknowledgments
This work is supported in part by the Guangzhou Key Lab of Body Data Science (201605030011) and the Diabetes Intelligent Wear Monitoring Equipment and Complications Prevention and Control Cloud Platform (2016B010108008) and the Research and Application of Mobile Medical Technology (2015B010106008).
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Tian, X. et al. (2018). A Novel Computer-Aided Diagnosis Method of Nasopharyngeal Carcinoma Based on Magnetic Resonance Images. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_21
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DOI: https://doi.org/10.1007/978-981-10-8530-7_21
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