%0 Journal Article %1 IJACSA.2012.030503 %A Kohei Arai Indra Nugraha Abdullah, Hiroshi Okumura %D 2012 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Automated Detection Deviation. Features; Mammogram; Method; Micro Standard Statistical Textural calcification; %N 5 %T Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features %U http://ijacsa.thesai.org/ %V 3 %X 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.

@article{IJACSA.2012.030503, 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-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.}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Kohei Arai Indra Nugraha Abdullah}, Hiroshi Okumura}, biburl = {https://www.bibsonomy.org/bibtex/269c827c155494423a0d583a3f6f43e4e/thesaiorg}, interhash = {74a3cb87087c022ac804f1d39451375a}, intrahash = {69c827c155494423a0d583a3f6f43e4e}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Automated Detection Deviation. Features; Mammogram; Method; Micro Standard Statistical Textural calcification;}, number = 5, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features}}, url = {http://ijacsa.thesai.org/}, volume = 3, year = 2012 }