國立臺灣大學資訊工程學系Chen, Wei-MingWei-MingChenChang, Ruey-FengRuey-FengChangMoon, Woo-KtungWoo-KtungMoonChen, Dar-RenDar-RenChen2006-09-272018-07-052006-09-272018-07-052003http://ntur.lib.ntu.edu.tw//handle/246246/20060927122917164483Because ultrasound (US) imaging offers benefits compared with other medical imaging techniques, it is used routinely in nearly all hospitals and many clinics. However, the surface features and internal structure of a tumor are not easily demonstrated simultaneously using the traditional 2-D US. The newly developed three-dimensional (3-D) US can capture the morphology of a breast tumor and overcome the limitations of the traditional 2-D US. This study deals with pixel relation analysis techniques for use with 3-D breast US images and compares its performance to 2-D versions of the images. The 3-D US imaging was performed using a Voluson 530 scanner. The rectangular subimages of the volume-of-interest (VOI) were manually selected and the selected VOIs were outlined to include the entire extent of the tumor margin. The databases in this study included 54 malignant and 161 benign tumors. All solid nodules at US belong over C3 (probably benign) according to ACR BI-RADS category. All or some selected 2-D slices were used separately to calculate the diagnosis features for a 3-D US data set. We have proposed and compared several different methods to extract the characteristics of these consecutive 2-D images. As shown in our experiments, the diagnostic results were better than those of the conventional 2-D US. In the experiments, the area index Az under ROC curve of the proposed 3-D US method can achieve 0.9700 0.0118, but Az of the 2-D US is only 0.8461 0.0315. The p value of these two Az differences using z test is smaller than 0.01. Furthermore, we can find that the features from only several slices are enough to provide good diagnostic results if the adopted features are modified from the 2-D features. (E-mail:dlchen88@ms13.hinet.net)application/pdf353662 bytesapplication/pdfzh-TW3-D breast ultrasoundPixel relation analysisBreast tumorNeural network[SDGs]SDG3journal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927122917164483/1/umb0307-1.pdf