Abstract: The MRI and mammogram texture analysis matrix itself does not directly provide a single feature that may be used for texture discrimination. Instead, the matrix can be used as a representation scheme for the texture image and the features are computed. Feature selection is focused on many areas, especially in artificial intelligence, medical image processing, Data Mining [Dom et al.] and pattern recognition. Classification of objects is an important area of research and of practical applications in a variety of fields, including pattern recognition, artificial intelligence and vision analysis. Classifier design can be performed with labelled or unlabelled data.
Keywords: Magnetic Resonance Imaging (MRI), Surrounding Region Dependency Matrix, Spatial Gray Level Dependency Matrix, Feature Selection.
Downloads:
|
DOI:
10.17148/IJARCCE.2020.9636
[1] A. Sivaramakrishnan*, M. Asmi, "Enhanced Feature Extraction, Selection and Classification of MRI and Mammogram Texture Analysis," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.9636
