research-article

Image modality classification: a late fusion method based on confidence indicator and closeness matrix

Authors:

Apostol Natsev,

Yue PanAuthors Info & Claims

ICMR '11: Proceedings of the 1st ACM International Conference on Multimedia Retrieval

Article No.: 55, Pages 1 - 7

https://doi.org/10.1145/1991996.1992051

Published: 18 April 2011 Publication History

Abstract

Automatic recognition or classification of medical image modality can provide valuable information for medical image retrieval and analysis. In this paper, we discuss an application of SVM ensemble classifiers to the problem, and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes. Using a matrix of closeness and a set of additional fusion rules, the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results. An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach.

References

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Depeursinge A., and Muller, H., Fusion Techniques for Combining Textual and Visual Information Retrieval, in ImageCLEF Experimental Evaluation in Visual Information Retrieval, H. Muller, P. Clough, Th. Deselaers, B. Caputo (eds). Springer, September 2010. 95--114.

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Kalpathy-Cramer, J., and Hersh, W., Multimodal Medical Image Retrieval - Image Categorization to Improve Search Precision. MIR'10, 2010, 165--173.

Digital Library

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Muller, H., Kalpathy--Cramer, J., Eggel, I., et al. Overview of the CLEF 2010 medical image retrieval track. CLEF2010 Notebook Papers, 2010.

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Xie, L., Natsev, N., Hill, M., et al. The Accuracy and Value of Machine-Generated Image Tags - Design and User Evaluation of an End-to-End Image Tagging System. In Proceedings of ACM International Conference on Image and Video Retrieval (CIVR2010). 2010. 58--65.

Digital Library

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Natsev, N., Hill, M., Smith, JR. et al. IBM Research TRECVID-2009 Video Retrieval System, 2009.

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Uwimana, E., Automatic classification of medical images for Content Based Image Retrieval Systems (CBIR), Human Factors and Ergonomics Society Annual Meeting Proceedings, Volume 52, Number 12, 2008, pp. 788--792.

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Ayache, S., Quenot, G., Gensel, J., Classifier fusion for SVM-based multimedia semantic indexing, ECIR'07 Proceedings of the 29th European conference on IR research, Lecture Notes in Computer Science, 2007, Volume 4425/2007, 494--504.

Digital Library

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Kalpathy-Cramer, J., Hersh, W., Automatic Image Modality Based Classification and Annotation to Improve Medical Image Retrieval. MEDINFO 2007, K. Kuhn et al. (Eds) IOS Press, 2007, 1334--1338.

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Yan, R. Tesic, J., and Smith, JR., Model-shared subspace boosting for multi-label classification. In Proc. ACM KDD, pages 834--843, 2007.

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Florea, F., Müller, H., Rogozan R., et al. Medical image categorization with MedIC and MedGIFT. MIE2006.

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Greenspa, H., Pinhas, AT., Medical Image Categorization and Retrieval For PACS Using the GMM-KL Framework. IEEE Transactions on Information Technology in BioMedicine, 2005. 190--202.

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Pourghassem, H., Ghassemian, H., Content-based medical image classification using a new hierarchical merging scheme. Journal of Computerized Medical Image and Graphics, 2008.

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www.imageclef.org/2010/medical

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IBM Multimedia Analysis and Retrieval System. http://www.alphaworks.ibm.com/tech/imars

Cited By

Lee SZare M(2018)Biomedical compound figure detection using deep learning and fusion techniquesIET Image Processing10.1049/iet-ipr.2017.080012:6(1031-1037)Online publication date: Jun-2018
https://doi.org/10.1049/iet-ipr.2017.0800
Khan SYong SDeng J(2015)Ensemble classification with modified SIFT descriptor for medical image modality2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2015.7761517(1-6)Online publication date: Nov-2015
https://doi.org/10.1109/IVCNZ.2015.7761517
Wu HHe L(2015)Combining visual and textual features for medical image modality classification with ℓp−norm multiple kernel learningNeurocomputing10.1016/j.neucom.2014.06.046147(387-394)Online publication date: Jan-2015
https://doi.org/10.1016/j.neucom.2014.06.046

Index Terms

Image modality classification: a late fusion method based on confidence indicator and closeness matrix

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Information

Published In

cover image ACM Conferences

ICMR '11: Proceedings of the 1st ACM International Conference on Multimedia Retrieval

April 2011

512 pages

ISBN:9781450303361

DOI:10.1145/1991996

General Chairs:
Francesco G. B. De Natale
University of Trento, Italy
,
Alberto Del Bimbo
University of Florence, Italy
,
Program Chairs:
Alan Hanjalic
University of Amsterdam, Netherlands
,
B. S. Manjunath
University of California, Santa Barbara
,
Shin'ichi Satoh
NII, Japan

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2011

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ICMR'11

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SIGMM

ICMR'11: International Conference on Multimedia Retrieval

April 18 - 20, 2011

Trento, Italy

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Lee SZare M(2018)Biomedical compound figure detection using deep learning and fusion techniquesIET Image Processing10.1049/iet-ipr.2017.080012:6(1031-1037)Online publication date: Jun-2018
https://doi.org/10.1049/iet-ipr.2017.0800
Khan SYong SDeng J(2015)Ensemble classification with modified SIFT descriptor for medical image modality2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2015.7761517(1-6)Online publication date: Nov-2015
https://doi.org/10.1109/IVCNZ.2015.7761517
Wu HHe L(2015)Combining visual and textual features for medical image modality classification with ℓp−norm multiple kernel learningNeurocomputing10.1016/j.neucom.2014.06.046147(387-394)Online publication date: Jan-2015
https://doi.org/10.1016/j.neucom.2014.06.046

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