Open AccessUpgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues
Author(s) -
B. Saroja,
A. Selwin Mich Priyadharson
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.20.14781
Subject(s) - colorectal cancer , pattern recognition (psychology) , gray level , fractal dimension , artificial intelligence , biopsy , medicine , texture (cosmology) , mathematics , fractal , pathology , image (mathematics) , computer science , cancer , mathematical analysis
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.