Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9911
Title: Global context inference for adaptive abnormality detection in PET-CT images
Authors: Song, Y
Cai, W
Feng, DD
Issue Date: 2012
Source: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2-5 May 2012, Barcelona, p. 482-485
Abstract: PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global contexts, which together with local features are then classified to produce the detection results adaptively. The proposed method is evaluated on clinical PET-CT images from NSCLC studies, and high detection precision and recall are demonstrated.
Keywords: PET-CT
Abnormality
Detection
Global contexts
Max-margin
Publisher: IEEE
ISBN: 978-1-4577-1857-1
ISSN: 1945-7928
DOI: 10.1109/ISBI.2012.6235589
Appears in Collections:Conference Paper

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