Enhanced CT Images by the Wavelet Transform Improving Diagnostic Accuracy of Chest Nodules View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-11-24

AUTHORS

Xiuhua Guo, Xiangye Liu, Huan Wang, Zhigang Liang, Wei Wu, Qian He, Kuncheng Li, Wei Wang

ABSTRACT

The objective of this study was to compare the diagnostic accuracy in the interpretation of chest nodules using original CT images versus enhanced CT images based on the wavelet transform. The CT images of 118 patients with cancers and 60 with benign nodules were used in this study. All images were enhanced through an algorithm based on the wavelet transform. Two experienced radiologists interpreted all the images in two reading sessions. The reading sessions were separated by a minimum of 1 month in order to minimize the effect of observer’s recall. The Mann–Whitney U nonparametric test was used to analyze the interpretation results between original and enhanced images. The Kruskal–Wallis H nonparametric test of K independent samples was used to investigate the related factors which could affect the diagnostic accuracy of observers. The area under the ROC curves for the original and enhanced images was 0.681 and 0.736, respectively. There is significant difference in diagnosing the malignant nodules between the original and enhanced images (z = 7.122, P < 0.001), whereas there is no significant difference in diagnosing the benign nodules (z = 0.894, P = 0.371). The results showed that there is significant difference between original and enhancement images when the size of nodules was larger than 2 cm (Z = −2.509, P = 0.012, indicating the size of the nodules is a critical evaluating factor of the diagnostic accuracy of observers). This study indicated that the image enhancement based on wavelet transform could improve the diagnostic accuracy of radiologists for the malignant chest nodules. More... »

PAGES

44-49

References to SciGraph publications

  • 2003-04-14. An Approach to Adaptive Enhancement of Diagnostic X-Ray Images in EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10278-009-9248-y

    DOI

    http://dx.doi.org/10.1007/s10278-009-9248-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1034069608

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19937084


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