@article{digilib19346,
          volume = {Vol. 8},
           month = {February},
          author = {DKK SHOFWATUL ?UYUN},
           title = {COMPARISON BETWEEN AUTOMATIC AND SEMIAUTOMATIC THRESHOLDING METHOD FOR MAMMOGRAPHIC DENSITY CLASSIFICATION},
       publisher = {www.ttp.net.},
         journal = {Advanced Materials Research},
           pages = {672--675},
            year = {2014},
        keywords = {mammographic density classification, breast cancer risk, automatic threshold, semi automatic threshold},
             url = {https://digilib.uin-suka.ac.id/id/eprint/19346/},
        abstract = {Mammographic density is a novel independent risk factor of breast cancer that reflects
the amount of fibroglandular tissue. Breast Imaging Reporting and Data System (BIRADS) density
is one of the mammographic density classification schemes which are most widely used by
radiologists. Initially, the method used for assessing mammographic density was subjective and
qualitative. Recently however, the measurement of mammographic density is more objective and
quantitative. In this paper, we propose an alternative model of breast cancer risk factor assessment
based on a quantitative approach of density mammogram. This model consists of pre-processing,
breast area counting, fibroglandular tissue area counting that uses maximum entropy and multilevel
thresholds, and finally breast density counting to determine the risk classification of breast cancer.
The proposed model has been tested on a private database from Oncology Clinic Kotabaru,
Yogyakarta, Indonesia consisting of 30 mammograms and has been analyzed by some radiologists
using the semiautomatic threshold. The result shows that percentage of mammographic density
counted by maximum entropy threshold method has the accuracy, sensitivity and specificity of
about 67\%, 50\% and 75\% respectively compared to the semiautomatic thresholding method. On the
other hand, the accuracy, sensitivity and specificity resulted from using multilevel threshold is
about 93\%, 87\% and 95\% respectively. The obtained results suggest that multilevel threshold is
perfectly suited for getting quantitative measurement of mammographic density as one of the
strongest risk factors for breast cancer.}
}