Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31918
Title: A content-based image retrieval framework for multi-modality lung images
Authors: Song, Y
Cai, W
Eberl, S
Fulham, M
Feng, D
Keywords: Content-based retrieval
Feature extraction
Image retrieval
Lung
Positron emission tomography
Support vector machines
Issue Date: 2010
Publisher: IEEE
Source: 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), 12-15 October 2010, Perth, WA, p. 285-290 How to cite?
Abstract: This paper presents a framework for effective and fast content-based image retrieval for multi-modality PET-CT lung scans. PET-CT scans present significant advantages in tumor staging, but also place new challenges in computerized image analysis and retrieval. Our framework comprises 5 major components: lung field estimation, texture feature extraction, feature categorization, refinement using SVM, and similarity measure. Clinical data from lung cancer patients are used as case studies, and effective retrieval performance is demonstrated.
URI: http://hdl.handle.net/10397/31918
ISBN: 978-1-4244-9167-4
ISSN: 1063-7125
DOI: 10.1109/CBMS.2010.6042657
Appears in Collections:Conference Paper

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