Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

  • Authors

    • B Saroja
    • A Selwin Mich Priyadharson
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.14781
  • Colorectal cancer, textural features, U-SGLDM, fractal features, histopathological images.

  • Abstract

    Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.

     

     

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  • How to Cite

    Saroja, B., & Selwin Mich Priyadharson, A. (2018). Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues. International Journal of Engineering & Technology, 7(2.20), 291-294. https://doi.org/10.14419/ijet.v7i2.20.14781

    Received date: 2018-06-29

    Accepted date: 2018-06-29

    Published date: 2018-04-18