Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

  • Authors

    • Aravinda H.L
    • M.V Sudhamani
    https://doi.org/10.14419/ijet.v7i2.6.11269

    Received date: April 8, 2018

    Accepted date: April 8, 2018

    Published date: March 11, 2018

  • Liver tumors, Average Correction Higher order Local Autocorrelation Coefficient (ACHLAC), Legendre Moments (LM)

  • Abstract

    The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

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  • How to Cite

    H.L, A., & Sudhamani, M. (2018). Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments. International Journal of Engineering and Technology, 7(2.6), 306-310. https://doi.org/10.14419/ijet.v7i2.6.11269