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Classification of the liver tumors Using Co-Occurrence Matrices of textural Microstructures

Classification of the liver tumors Using Co-Occurrence Matrices of textural Microstructures
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出处 《通讯和计算机(中英文版)》 2015年第1期6-12,共7页 Journal of Communication and Computer
关键词 共生矩阵 肝肿瘤 分类 ADABOOST 肝细胞肝癌 质地 纹理模型 显微组织 Texture, textural microstructure co-occurrence matrices, liver tumors, ultrasound images, classification performance.
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参考文献20

  • 1American liver foundation, 2013. Online: http://www. liverfoundation.org/abouttheliver/in fo/benigntumors/.
  • 2Mitrea, D., Mitrea, P., Nedevschi, S., and Badea, R. 2012. "Abdominal Tumor Characterization and Recognition Using Superior Order Cooccurrence Matrices, Based on Ultrasound Images." Computational and Mathematical Methods in Medicine: 1 - 17.
  • 3Mitrea, D., Nedevschi, S., Socaciu, M., and Badea, R. 2012. "The Role of the Superior Order GLCM in the Characterization and Recognition of the Liver Tumors from Ultrasound Images." Radioengineering 21 (April): 79-85.
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  • 5Toennies, K. 2012. Guide to Medical Image Analysis. Methods and Algorithms. London: Springer Verlag.
  • 6Laws, K. 1. 1980. "Rapid Texture Identification." SPIE lmage Processing for Missile Guidance 238.' 76-380.
  • 7Witten, 1., and Frank, E. 2005. Data Mining-Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • 8Leung, T., and Malik, J. 2001. "Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons." International Journal of Computer Vision 43: 29-44.
  • 9Sujana, H., and Swamamani, S. 1996. "Application of Artificial Neural Networks for the Classification of Liver Lesions by Texture Parameters." Ultrasound in Medicine & Biology 22:1177-81.
  • 10Chikui, T. 2005. "Sonographic Texture Characterization of Salivary Gland Tumors by Fractal Analysis." Ultrasound in Medicine & Biology 31 : 1297- 1304.

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