Lo C.-M.Chan S.-W.YA-WEN YANGYEUN-CHUNG CHANGCHIUN-SHENG HUANGJou Y.-S.RUEY-FENG CHANG2020-03-232020-03-23201603015629https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957837834&doi=10.1016%2fj.ultrasmedbio.2015.12.006&partnerID=40&md5=e21361b207cc94e9307557682c968edahttps://scholars.lib.ntu.edu.tw/handle/123456789/477728A tumor-mapping algorithm was proposed to identify the same regions in different passes of automated breast ultrasound (ABUS). A total of 53 abnormal passes with 41 biopsy-proven tumors and 13 normal passes were collected. After computer-aided tumor detection, a mapping pair was composed of a detected region in one pass and another region in another pass. Location criteria, including the radial position as on a clock, the relative distance and the distance to the nipple, were used to extract mapping pairs with close regions. Quantitative intensity, morphology, texture and location features were then combined in a classifier for further classification. The performance of the classifier achieved a mapping rate of 80.39% (41/51), with an error rate of 5.97% (4/67). The trade-offs between the mapping and error rates were evaluated, and Az = 0.9094 was obtained. The proposed tumor-mapping algorithm was capable of automatically providing location correspondence information that would be helpful in reviews of ABUS examinations. ? 2016 World Federation for Ultrasound in Medicine & Biology.Automated breast ultrasound; Breast cancer; Computer-aided detection; Tumor mapping[SDGs]SDG3Algorithms; Automation; Conformal mapping; Economic and social effects; Location; Tumors; Ultrasonic applications; Breast Cancer; Breast ultrasound; Computer aided detection; Feasibility testing; Mapping algorithms; Radial position; Relative distances; Tumor detection; Mapping; adult; aged; algorithm; analytical error; Article; automated breast ultrasound; breast tumor; classifier; clinical article; echomammography; feasibility study; female; human; nipple; priority journal; quantitative analysis; quantitative intensity; quantitative location; quantitative morphology; quantitative texture; real time ultrasound scanner; retrospective study; tumor localization; tumor mapping; algorithm; automated pattern recognition; cancer staging; computer assisted diagnosis; diagnostic imaging; echomammography; image enhancement; image subtraction; machine learning; middle aged; pathology; procedures; reproducibility; sensitivity and specificity; three dimensional imaging; tumor volume; Adult; Aged; Algorithms; Breast Neoplasms; Feasibility Studies; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Machine Learning; Middle Aged; Neoplasm Staging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Tumor Burden; Ultrasonography, Mammaryjournal article10.1016/j.ultrasmedbio.2015.12.006268254682-s2.0-84957837834