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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 6/2012

01-12-2012

Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images

Authors: Wei Yang, Zhentai Lu, Mei Yu, Meiyan Huang, Qianjin Feng, Wufan Chen

Published in: Journal of Imaging Informatics in Medicine | Issue 6/2012

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Abstract

This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
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Metadata
Title
Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images
Authors
Wei Yang
Zhentai Lu
Mei Yu
Meiyan Huang
Qianjin Feng
Wufan Chen
Publication date
01-12-2012
Publisher
Springer-Verlag
Published in
Journal of Imaging Informatics in Medicine / Issue 6/2012
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-012-9495-1

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