Detection ofLobular Carcinoma In Situ(LCIS)by Image Analysis
In this study, we aimed to develop a quantitative image analysis method that may enhance the detection of the lobular carcinoma in-situ (LCIS) in breast cancer specimens. Glandular areas were segmented by using mathematical morphology from 5X histologic images of breast cancer cases (n=213). Computational features such as shape, intensity, and texture were extracted from glandular areas. Segmented glandular areas of LCIS were significantly larger, thicker, lower and less variant in intensity, compared to normal areas (Mann-Whitney test, p<0.01). Our models based on data mining algorithms detected LCIS frames at the accuracy rate close to 99％. Our proposed methods may be well incorporated into a further development of computer aided detection (CAD) software for the screening of whole slide images to locate the LCIS areas.
Image Analysis Pattern Recognition Whole Slide Images Breast Cancer Lobular Carcinoma In-situ (LCIS) Computer Aided Detection
Sujin Kim DesokKim Hyun-Joo Choi Hee Jae Joo
Division of Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, S Department of Pathology, St. Vincents Hospital, The Catholic University, Suwon, South Korea Department of Pathology, Ajou University Hospital Suwon, South Korea
国际会议
上海
英文
252-255
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）