Self-Organization Map Based Segmentation of Breast Cancer

Authors

  • A. Arokiyamary Delphina Research Scholar, Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India
  • M. Kamarasan Assistant Professor, Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India
  • S. Sathiamoorthy Assistant Professor, Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajeat-2018.7.2.1015

Keywords:

Breast Cancer, Mammography, Self-Organizing Map, Euclidean Distance, Validity Measure, Double Bouldin Index

Abstract

Breast cancer is second major leading cause of cancer fatality in women. Mammography prevails best method for initial detection of cancers of breast, capable of identifying small pieces up to two years before they grow large enough to be evident on physical testing. X-ray images of breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, Radio-graphic images into regions of similar texture is usually performed during method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and implied transition from one texture to makes segmentation remarkably hard. The task of analyzing different texture areas can be considered form of exploratory report since priori awareness about number of different regions in image is not known. This paper presents a segmentation method by utilizing SOM.

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Published

18-07-2018

How to Cite

Arokiyamary Delphina, A., Kamarasan, M., & Sathiamoorthy, S. (2018). Self-Organization Map Based Segmentation of Breast Cancer. Asian Journal of Engineering and Applied Technology, 7(2), 31–36. https://doi.org/10.51983/ajeat-2018.7.2.1015