COMPUTATION MODEL DETERMINATION OF RISK FACTORS FOR BREAST CANCER BASED ON PATTERNS AND PERCENTAGE OF DENSITY Mammography
ABSTRACT: Mammographic density is a reflection of the composition of the fibroglandular tissue that is a strong risk factor for breast cancer. In addition to the risk factors of breast cancer, mammography with high density has greater likelihood of not detecting breast cancer because the tumor is covered by the density (the masking effect). The determination of mammographic density can be done both qualitatively and quantitatively to reduce subjectivity. Therefore, it is necessary to develop a computational model for the determination of risk factors for breast cancer based on the pattern and the percentage of mammographic density. The research aims at developing two models of the calculation of breast cancer risk tried out using mammogram from Onkology cliniqe in Kotabaru, Yogyakarta and Mammographic Image Analysis Society (MIAS). Firstly, the steps to classify the risk based on mammography density pattern consist of RoI selection, pre-proccessing, extracting statistic feature of first and second orde (GLCM), JST classification and model evaluation. Secondly, the steps of risk classification based on the percentage of mammography density consist of pre-processing, the selection of thresholding method, mammogram segmentation to get breast area and fibroglandular tissue area, density percentage calculation, classifying the risk level and model evaluation. There are four features (ASM, IDM, variance and entropy) has significance very strong against determination level risks of breast cancer. The use of the fourth feature on the computation model of the pattern mammography density with 16 descriptor (4 features x 4 direction) as node input on JST have accuracy, specificity and sensitivity of about 90%, 85% and 95% respectively. While the comparison performance thresholding methods to get breast area and fibroglandular tissue area on the computation model of the percentage of mammography density showed that the multilevel thresholding and algorithm zack has better performance. Performance evaluation model of two thresholding methods have the accuracy, sensitivity and specificity of about 91%, 87% and 95% respectively.
ABSTRACT: Mammographic density is a reflection of the composition of the fibroglandular tissue that is a strong risk factor for breast cancer. In addition to the risk factors of breast cancer, mammography with high density has greater likelihood of not detecting breast cancer because the tumor is covered by the density (the masking effect). The determination of mammographic density can be done both qualitatively and quantitatively to reduce subjectivity. Therefore, it is necessary to develop a computational model for the determination of risk factors for breast cancer based on the pattern and the percentage of mammographic density. The research aims at developing two models of the calculation of breast cancer risk tried out using mammogram from Onkology cliniqe in Kotabaru, Yogyakarta and Mammographic Image Analysis Society (MIAS). Firstly, the steps to classify the risk based on mammography density pattern consist of RoI selection, pre-proccessing, extracting statistic feature of first and second orde (GLCM), JST classification and model evaluation. Secondly, the steps of risk classification based on the percentage of mammography density consist of pre-processing, the selection of thresholding method, mammogram segmentation to get breast area and fibroglandular tissue area, density percentage calculation, classifying the risk level and model evaluation. There are four features (ASM, IDM, variance and entropy) has significance very strong against determination level risks of breast cancer. The use of the fourth feature on the computation model of the pattern mammography density with 16 descriptor (4 features x 4 direction) as node input on JST have accuracy, specificity and sensitivity of about 90%, 85% and 95% respectively. While the comparison performance thresholding methods to get breast area and fibroglandular tissue area on the computation model of the percentage of mammography density showed that the multilevel thresholding and algorithm zack has better performance. Performance evaluation model of two thresholding methods have the accuracy, sensitivity and specificity of about 91%, 87% and 95% respectively.
