작성자 M.I.D
[논문] Development of Automatic Cluster Algorithm for Microcalcification…
본문
Development of Automatic Cluster Algorithm for Microcalcification in Digital Mammography
(디지털 유방영상에서 미세석회화의 자동군집화 기법 개발)
Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)
Volume 32 Issue 1 / Pages.45-52 / 2009 / 2288-3509(pISSN) / 2384-1168(eISSN)
Korean Society of Radiological Science (대한방사선과학회)
Choi, Seok-Yoon (Dept. of Medical Engineering, Korea University) ; Kim, Chang-Soo (Dept. of Radiological Science, College of Health Sciences, Catholic University of Pusan)
최석윤 (고려대학교 대학원 의공학협동) ; 김창수 (부산가톨릭대학교 보건과학대학 방사선학과)
Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.
(디지털 유방영상에서 미세석회화의 자동군집화 기법 개발)
Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)
Volume 32 Issue 1 / Pages.45-52 / 2009 / 2288-3509(pISSN) / 2384-1168(eISSN)
Korean Society of Radiological Science (대한방사선과학회)
Choi, Seok-Yoon (Dept. of Medical Engineering, Korea University) ; Kim, Chang-Soo (Dept. of Radiological Science, College of Health Sciences, Catholic University of Pusan)
최석윤 (고려대학교 대학원 의공학협동) ; 김창수 (부산가톨릭대학교 보건과학대학 방사선학과)
Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.