A New Semantic Annotation Method for Chest X-Ray Images

For the purpose of taking good use of the diagnosis obtained from medical experts and improving the accuracy of chest X-ray images retrieval, the lung fields are segmented and interested regions are marked off on the basis of chest X-ray images having been processed previously; the Gray Difference S...

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Bibliographic Details
Published in2010 2nd International Conference on Information Engineering and Computer Science pp. 1 - 4
Main Authors Wencheng Cui, Mengjia Xu, Shaozhu Li, Hong Shao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Active shape model
Biomedical imaging
Entropy
Feature extraction
Lungs
Semantics
X-ray imaging
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Summary:For the purpose of taking good use of the diagnosis obtained from medical experts and improving the accuracy of chest X-ray images retrieval, the lung fields are segmented and interested regions are marked off on the basis of chest X-ray images having been processed previously; the Gray Difference Statistics is used to indicate the texture feature of each region. Using the K-nearest neighbor classifier, the texture features are mapped respectively to the standard image classes pre-described by the experts, thus it realizes the semantic annotation of regions in the whole image. This method can not only narrow the semantic gap between the low-level features and the high-level semantics of images effectively, but also has an active effect on improving the efficiency of medical diagnosis.
ISBN:1424479398
9781424479399
ISSN:2156-7379
DOI:10.1109/ICIECS.2010.5677691