Abstract
Aiming at the medical monitoring problem, the thyroid image features are extracted in this paper and the human thyroid CT image is classified. Firstly, the area of the two thyroid leaves and the cross section of thyroid biopsy are used as original features. Secondly, the area ratio of the two thyroid leaves and the cross section area of the two thyroid leaves are calculated and serve as the new features which describes the pattern of human thyroid. After the feature extraction, theK-nearest neighbor classification algorithm is adopted to distinguish images belonging to the normal or the abnormal categories respectively. The experimental result shows that the feature extraction method is suitable and the recognition result is generated with a certain accuracy, reliability and practicability when these two features to classify the normal and abnormal categories.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, M., Wu, X., Xu, J. (2012). Thyroid Image Feature Extraction and State Recognition. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_35
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DOI: https://doi.org/10.1007/978-3-642-31919-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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