Suppr超能文献

[ROC analysis in post-processing of image data in digital thoracic radiography].

作者信息

Müller R D, Hirche H, Voss M, Buddenbrock B, John V, Gocke P

机构信息

Zentralinstitut für Röntgendiagnostik des Universitätsklinikums-GHS Essen.

出版信息

Rofo. 1995 Feb;162(2):163-9. doi: 10.1055/s-2007-1015854.

Abstract

PURPOSE

To examine the extent to which digital luminescence radiography (DLR) can be used for the imaging of pulmonary nodules and interstitial lung disease in chest radiography without any loss of image quality. Additionally: to examine whether post-processing of image data can optimise the recognizability of varied image details.

MATERIALS AND METHODS

Detail perceptibility studies were performed on an anthropomorphic thorax phantom with simulated nodules and small linear and reticular details. Under standard conditions, digital luminescence radiographs were obtained in 7 different image modes, and these were compared with a 200-speed screen-film system. The detection of these systems was evaluated in an ROC analysis on the basis of 19,200 individual observations.

RESULTS

Edge enhancement or application of high-frequency-enhancing small filter kernels (S 5) slightly improves the detection of linear structures; however, the illustration of nodular details is markedly reduced. Larger filter kernels (S 20, S 40) make a definitive detection possible--not only of circular, but also of linear details.

CONCLUSIONS

Storage phosphor radiographs are equal to the tested analog screen-film-system. The optimization of post-processing can be helpful in the prevention of routine multiple documentations.

摘要

相似文献

1
[ROC analysis in post-processing of image data in digital thoracic radiography].
Rofo. 1995 Feb;162(2):163-9. doi: 10.1055/s-2007-1015854.
9
Assessment and optimisation of the image quality of chest-radiography systems.
Radiat Prot Dosimetry. 2005;114(1-3):264-8. doi: 10.1093/rpd/nch559.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验