Objectives: To compare the application value of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in breast galactography. Materials and Methods: A total of 128 patients with pathological nipple discharge (PND) were selected to undergo galactography. DBT and FFDM were performed for each patient after injecting the contrast agent; the radiation dose of DBT and FFDM was calculated, and the image quality was evaluated in consensus by two senior breast radiologists. Histopathologic data were found in 49 of the 128 patients. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for both FFDM- and DBT-galactography were calculated using histopathologic results as a reference standard. Data were presented as percentages along with their 95% confidence intervals (CI). Results: The average age of the 128 patients was 46.53 years. The average glandular dose (AGD) of DBT-galactography was slightly higher than that of FFDM-galactography (p < 0.001). DBT-galactography was 30.7% higher than FFDM-galactography in CC view, while DBT-galactography increased by 21.7% compared with FFDM-galactography in ML view. Regarding catheter anatomic distortion, structure detail, and overall image quality groups, DBT scores were higher than FFDM scores, and the differences were significant for all measures (p < 0.05). In 49 patients with pathological nipple discharge, we found that the DBT-galactography had higher sensitivity, specificity, PPV, and NPV (93.3%, 75%, 97.7%, and 50%, respectively) than FFDM-galactography (91.1%, 50%, 95.3%, and 33.3%, respectively). Conclusions: Compared to FFDM-galactography, within the acceptable radiation dose range, DBT-galactography increases the sensitivity and specificity of lesion detection by improving the image quality, providing more confidence for the diagnosis of clinical ductal lesions.
We present a method to locate the caption area of frames in videos. Histogram can be extracted easily from an image, while the color of caption area is apparently brighter than other areas. Unlike previously published methods of using edge detection methods to detect the edge of caption from images, this method uses the caption histogram to detect the caption area, and applies the probabilistic theory to determine the area based on the caption histogram. Experimental results show that our method is practicable and prosperous.
The Bayesian networks can express the joint probabilistic distribution compactly between variables and can express the conditionally independence conveniently. The joint probabilistic influence from the parents to their child can be got from the Bayesian network structure however parents are not necessarily have common influence to their child, which are called by the name of causal influence independence other than conditional independence.The causal influence independence extension model of Bayesian networks presented can have wider meaning than traditional Bayesian networks, which is more applicable and easier to understand.
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