Detection of Abnormalities in Digital Mammogram Images by Analysing Optical Parameters
R. J. Hemalatha1, A. Akalya2, R. Jeyanthi3, T.R.Thamizhvani4, Josephin Arockia Dhivya5
1R.J.Hemalatha*, head & Assistant Professor -Department of Biomedical Engineering,Vels Institute of Science, Technology & Advanced Studies, Chennai, India.
2A.Akalya-Student -Department of Biomedical ,Engineering,Vels Institute of Science, Technology & Advanced Studies, Chennai, India.
3R.Jeyanthi, Student -Department of Biomedical Engineering,Vels Institute of Science, Technology & Advanced Studies, Chennai, India.
4T.R.Thamizhvani, Assistant Professor -Department of Biomedical Engineering,Vels Institute of Science, Technology & Advanced Studies, Chennai, India.
5Josephin Arockia Dhivya, Assistant Professor Department of Biomedical Engineering,Vels Institute of Science, Technology & Advanced Studies, Chennai, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12535-12540 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5404118419/2019©BEIESP | DOI: 10.35940/ijrte.D5404.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is one of the major life threatening cancer that tremendously affects women. According to the survey, the mortality rate of females is increasing due to the belated detection of breast cancer. In recent years, Mammograms plays a vital role in breast cancer detection. In this study, an algorithm is used to detect the microcalcification of the mammogram by calculating its optical characteristics. The considered optical characteristics are the reflection coefficient, absorption coefficient, and transmission coefficient are calculated for the binned mammogram digital images are calculated. The total conservation of energy condition, RC + TC + AC =1 is satisfied with the obtained results. For each image, statistical features are extracted, analyzed and are classified with the help of SVM and ANN classifier. This algorithm is verified with about 30% of MIAS database images. This proposed algorithm acts as a good classifier for the detection of microcalcification thereby considering three optical characteristics.
Keywords: Digital Mammogram Images, RoI (Region of Interest) optical Characteristics, classifier.
Scope of the Article: Image Security.