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  • 标题:Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features
  • 本地全文:下载
  • 作者:Kohei Arai ; Indra Nugraha Abdullah ; Hiroshi Okumura
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2012
  • 卷号:3
  • 期号:5
  • DOI:10.14569/IJACSA.2012.030503
  • 出版社:Science and Information Society (SAI)
  • 摘要: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-branch 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.
  • 关键词:thesai; IJACSA; thesai.org; journal; IJACSA papers; Automated Detection Method; Mammogram; Micro calcification; Statistical Textural Features; Standard Deviation.
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