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DOI: 10.14569/IJARAI.2012.010304
PDF

Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features

Author 1: Kohei Arai
Author 2: Indra Nugraha Abdullah
Author 3: Hiroshi Okumura

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 3, 2012.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branches standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.

Keywords: Automated Detection Method; Mammogram; Micro calcification; Statistical Textural Features; Standard Deviation.

Kohei Arai, Indra Nugraha Abdullah and Hiroshi Okumura, “Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(3), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010304

@article{Arai2012,
title = {Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010304},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010304},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {3},
author = {Kohei Arai and Indra Nugraha Abdullah and Hiroshi Okumura}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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