research-article

Towards the improvement of textual anatomy image classification using image local features

Authors:

Xiao Bing Huang,

Pierre TirillyAuthors Info & Claims

MMAR '11: Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval

Pages 25 - 30

https://doi.org/10.1145/2072545.2072551

Published: 29 November 2011 Publication History

Abstract

Image classification methods based on text utilize terms extracted from image annotations (image caption, image-related article, etc.) to achieve classification. For images involving different anatomical structures (chest, spine, etc.), however, the precision of pure textual classification often suffers from highly complex text contents (e.g. text terms extracted out of two MR abdomen images may be quite different from each other: terms from one image may concerns gastroenteritis while the other contains terms involving hysteromyoma). This paper tackles the anatomy image classification problem using a hybrid approach. First, a mutual information (MI) based filter is applied to select a set of terms with top MI scores for each anatomical class and help reduce the noise existing in the raw text. Second, local features extracted from the images are transformed as visual descriptors. Last, a hybrid scheme on the results from the textual and visual methods is applied to achieved further improvement of the classification results. Experiments show that this hybrid scheme improves the results over the sole textual or visual method on different anatomical class settings.

References

[1]

H. Alto, R. Rangayyantt, and J. Desautels. Content-based retrieval and analysis of mammographic masses. Journal Of Electronic Imaging, 14(2), 2005.

[2]

B. Andr, T. Vercauteren, A. Perchant, A. Buchner, M. Wallace, and N. Ayache. Introducing space and time in local feature-based endomicroscopic image retrieval. Medical Content-Based Retrieval for Clinical Decision Support, pages 18--30, 2010.

Digital Library

[3]

U. Avni, H. Greenspan, E. Konen, M. Sharon, and J. Goldberger. X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. IEEE Transactions on Medical Imaging, 30(3), 2011.

[4]

A. Bosch, X. Munoz, A. Oliver, and J. Marti. Modeling and classifying breast tissue density in mammograms. In CVPR, volume 2, pages 1552--1558, 2006.

Digital Library

[5]

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In European Conference on Computer Vision (ECCV) Workshop on Statistical Learning in Computer Vision, 2004.

[6]

CS@UWM. Software and dataset. http://guangzhou.cs.uwm.edu/med, 2011.

[7]

T. Deselaers, A. Hegerath, D. Keysers, and H. Ney. Sparse patch histograms for object classification in cluttered images. In DAGM Symposium, pages 202--211, 2006.

Digital Library

[8]

J. Dy, C. Brodley, A. Kak, L. Broderick, and A. Aisen. Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Trans. Pattern Anal. Mach. Intell., 25(3):373--378, 2003.

Digital Library

[9]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explorations, 11(1), 2009.

Digital Library

[10]

ImageCLEF. Image retrieval in clef: Medical retrieval task. http://www.imageclef.org/2010/medical, 2010.

[11]

J. Kalpathy-Cramer and W. Hersh. Automatic image modality based classification and annotation to improve medical image retrieval. In Proceedings of the 12th World Congress on Health (Medical) Informatics (MEDINFO), pages 1334--1338, 2007.

[12]

F.-F. Li and P. Perona. A bayesian hierarchical model for learning natural scene categories. In CVPR, volume 2, pages 524--531, 2005.

Digital Library

[13]

L. R. Long, S. Antani, D.-J. Lee, D. M. Krainak, and G. R. Thomas. Biomedical information from a national collection of spine x-rays: film to content-based retrieval. In Proceedings SPIE, pages 70--84, May 2003.

[14]

D. Lowe. Object recognition from local scale-invariant features. In ICCV, volume 2, 1999.

Digital Library

[15]

M. Lux and S. A. Chatzichristofis. Lire: lucene image retrieval: an extensible java cbir library. In Proceeding of the 16th ACM international conference on Multimedia, MM '08, pages 1085--1088, New York, NY, USA, 2008. ACM.

Digital Library

[16]

C. Manning, P. Raghavan, and H. Schuze. Introduction to Information Retrieval. Cambridge University Press, 2009.

Digital Library

[17]

C. D. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge University Press, July 2008.

Digital Library

[18]

H. Muller, J. Kalpathy-Cramer, I. Eggel, S. Bedrick, J. Reisetter, C. E. K. Jr., and W. Hersh. Overview of the CLEF 2010 medical image retrieval track. In Working notes of the Image-CLEF 2010 challenge, 2010.

[19]

H. Muller, C. Lovis, and A. Geissbuhler. The medgift project on medical image retrieval. Medical Imaging and Telemedicine, 2005.

[20]

C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis & Machine Intelligence, 19(5), 1997.

Digital Library

[21]

J. Sivic and A. Zisserman. Video google: a text retrieval approach to object matching in videos. In ICCV, volume 2, pages 1470--1477, 2008.

Digital Library

[22]

T. Tatiana, O. Francesco, and C. Barbara. Discriminative cue integration for medical image annotation. Pattern Recognition Letters, 29(15):1996--2002, November 2008.

Digital Library

[23]

P. Tirilly, K. Lu, X. Mu, T. Zhao, and Y. Cao. On modality classification and its use in text-based image retrieval in medical databases. In Proceedings of the 9th International Workshop on Content-based Multimedia Indexing, 2011.

[24]

T. Tommasi, F. Orabona, and B. Caputo. An svm confidence-based approach to medical image annotation. In proceedings of the 9th CLEF workshop 2008, 2008.

Digital Library

[25]

B. van Ginneken, L. Hogeweg, and M. Prokop. Computer-aided diagnosis in chest radiography: Beyond nodules. European Journal of Radiology, 72(2):226--230, 2009.

[26]

I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam, 2005.

Digital Library

[27]

Y. Y. Yao. Information-theoretic measures for knowledge discovery and data mining. Entropy Measures, Maximum Entropy Principle and Emerging Applications, pages 115--136, 2003.

Cited By

Rajendran TGnanasekaran T(2018)Multi level object relational similarity based image mining for improved image search using semantic ontologyCluster Computing10.1007/s10586-018-1975-8Online publication date: 1-Mar-2018
https://doi.org/10.1007/s10586-018-1975-8
Tripathi APandey SJangir H(2016)An improved and efficient image mining technique for classification of textual images using low-level image features2016 International Conference on Inventive Computation Technologies (ICICT)10.1109/INVENTIVE.2016.7823220(1-7)Online publication date: Aug-2016
https://doi.org/10.1109/INVENTIVE.2016.7823220
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
Show More Cited By

Index Terms

Towards the improvement of textual anatomy image classification using image local features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Conferences

MMAR '11: Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval

November 2011

70 pages

ISBN:9781450309912

DOI:10.1145/2072545

General Chairs:
Yu Cao
The University of Tennessee at Chattanooga, USA
,
Jayashree Kalpathy-Cramer
Oregon Health & Science University, USA
,
Devrim Ünay
Bahçesehir University, Istanbul, Turkey

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: 29 November 2011

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MM '11: ACM Multimedia Conference

November 29, 2011

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Cited By

Rajendran TGnanasekaran T(2018)Multi level object relational similarity based image mining for improved image search using semantic ontologyCluster Computing10.1007/s10586-018-1975-8Online publication date: 1-Mar-2018
https://doi.org/10.1007/s10586-018-1975-8
Tripathi APandey SJangir H(2016)An improved and efficient image mining technique for classification of textual images using low-level image features2016 International Conference on Inventive Computation Technologies (ICICT)10.1109/INVENTIVE.2016.7823220(1-7)Online publication date: Aug-2016
https://doi.org/10.1109/INVENTIVE.2016.7823220
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
Deepak KRai HSyed SKrishna PShyamasundar RShastri L(2012)Texture edge statistics for efficient retrieval of biomedical imagesProceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies10.1145/2459118.2459129(1-6)Online publication date: 23-Jan-2012
https://dl.acm.org/doi/10.1145/2459118.2459129

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