Earticle

초록

영어

In the present work, a computer aided classification system has been proposed for classification of mammogram images into normal, benign and cancer classes. The work has been carried out on thirty Digital Database for Screeningmammography(DDSM) cases consisting of 10 normal, 10 benign and 10 cancer images. The regions of interest (ROI) have been extracted from the right Medio Lateral Oblique (RMLO) part of the mammogram. We extracted 256×256 pixel size ROI from each case. Texture descriptors based on gray level co-occurrence method by varying the value of inter pixel distance ‘d’ from 1 to 8 have been used. The SVM classifier has been used for the classification task. The result of the study indicates that GLCM mean and range features computed at d=1 yield the maximum overall classification accuracy of 75% and 65 % respectively.

목차

Abstract
 1. Introduction
 2. Methodology
  A. Experimental Workflow
 3. Results and Discusssion
 4. Conclusion
 Reference

키워드

저자

• Dharmesh Singh [ ME Student EIED Thapar University, Patiala, Punjab, India ]
• Mandeep Singh [ Asst Prof EIED Thapar University,Patiala, Punjab, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605