An optimal reduced representation of a MoG with applications to medical image database classification

Jacob Goldberger; Hayit Greenspan; Jeremie Dreyfuss

doi:10.1109/cvpr.2007.383334

An optimal reduced representation of a MoG with applications to medical image database classification

Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfuss

Signal Processing

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the Unscented-Transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.

Original language	English
Title of host publication	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs	https://doi.org/10.1109/cvpr.2007.383334
State	Published - 2007
Event	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States Duration: 17 Jun 2007 → 22 Jun 2007

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Conference

Conference	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Country/Territory	United States
City	Minneapolis, MN
Period	17/06/07 → 22/06/07

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10.1109/cvpr.2007.383334

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Goldberger, J., Greenspan, H., & Dreyfuss, J. (2007). An optimal reduced representation of a MoG with applications to medical image database classification. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 Article 4270332 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/cvpr.2007.383334

@inproceedings{293dee482a784b8b87e5933e6ab54cda,

title = "An optimal reduced representation of a MoG with applications to medical image database classification",

abstract = "This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the Unscented-Transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.",

author = "Jacob Goldberger and Hayit Greenspan and Jeremie Dreyfuss",

year = "2007",

doi = "10.1109/cvpr.2007.383334",

language = "אנגלית",

isbn = "1424411807",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

booktitle = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07",

note = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 ; Conference date: 17-06-2007 Through 22-06-2007",

}

Goldberger, J, Greenspan, H & Dreyfuss, J 2007, An optimal reduced representation of a MoG with applications to medical image database classification. in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07., 4270332, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07, Minneapolis, MN, United States, 17/06/07. https://doi.org/10.1109/cvpr.2007.383334

An optimal reduced representation of a MoG with applications to medical image database classification. / Goldberger, Jacob; Greenspan, Hayit; Dreyfuss, Jeremie.
2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270332 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Goldberger J, Greenspan H, Dreyfuss J. An optimal reduced representation of a MoG with applications to medical image database classification. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270332. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/cvpr.2007.383334

An optimal reduced representation of a MoG with applications to medical image database classification

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