GLCM and GLRLM based Feature Extraction Technique in Mammogram Images

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammogram helps for early diagnosis before showing symptoms of cancer. The aim of this paper is to extract the various features of pre-processed mammogram images to improve the performance of the diagnosis, which helps the radiologists in reducing the false positive predictions. Mammogram images are pre-processed using hybrid filter MAX_AVM. Shape, Intensity, Gray Level Co-occurrence Matrix and Gray Level Run-Length Matrix features that help to represent the various classes of objects are extracted and used as inputs to the classifier. The classifier helps to classify the mammogram images into a normal or abnormal pattern. Experiments were conducted on MIAS database. The result shows that the combination of GLCM and GLRLM features are efficient and achieved the maximum classification accuracy rate when compared to other features.

     


  • Keywords


    Breast Cancer, Feature Extraction, GLCM, GLRLM, Mammogram, Neural Network.

  • References


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Article ID: 12378
 
DOI: 10.14419/ijet.v7i2.21.12378




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