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== Referenz ==
Yu Caoa, Henning Müller b, Charles E. Kahn, Jr.c, Ethan Munson: [[Multi-modal Medical Image Retrieval]]. Department of Computer Science & Engineering, University of Tennessee at Chattanooga

== DOI ==

== Abstract ==
Images are ubiquitous in biomedicine and the image viewers play a central role in many aspects of modern health care.
Tremendous amounts of medical image data are captured and recorded in digital format during the daily clinical
practice, medical research, and education (in 2009, over 117,000 images per day in the Geneva radiology department
alone). Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to
develop an effective and efficient medical image retrieval system for clinical practice and research. Traditionally,
medical image retrieval systems rely on text-based retrieval techniques that use the captions associated with the images,
and most often, the access is by patient ID, only. Since the 1990s, we have seen increasing interests in content-based
image retrieval for medical applications. One of the promising directions in content-based medical image retrieval is to
correlate multi-modal information (e.g., text and image information) to provide better insights.
In this paper, we concentrate our efforts on how to retrieve the most relevant medical images using multi-modal
information. Specifically, we use two modalities: the visual content of the images (represented by visual features) and
the textual information associated with the images. The core idea for multi-modal retrieval is rooted in information
fusion. Existing literature on multi-modal retrieval can roughly be classified into two categories: feature fusion and
retrieval fusion. The feature fusion strategy generates an integrated feature representation from multiple modalities. The
retrieval fusion strategy refers to the techniques that merge the retrieval results from multiple retrieval algorithms. Our
proposed approach belongs to the first category (feature fusion) and is largely inspired by Pham et al. [1] and Leinhart et
al.[2]. In [1], the features from different modalities are normalized and concatenated to generate the feature vectors.
Then, the Latent Semantic Analysis (LSA) is applied on these features for image retrieval. In [2], Lienhart et al propose
a multi-layer probability Latent Semantic Analysis (pLSA) to solve the multi-modal image retrieval problem. Our
proposed approach is different from Pham et al. [1] in that we do not simply concatenate the features from different
modalities. Instead, we represent the features from different modalities as a multi-dimensional matrix and incorporate
these feature vectors using an extended pLSA model. Our method is also different from Lienhart et al. [2] since we use a
single pLSA model instead of multiple pLSA models. The major contribution of our work is the new representation of
an image using visual-textual “words”. These “words” are generated from the visual descriptors and textual information
using the extended pLSA model.

== Extended Abstract ==

== Bibtex ==

== Used References ==
[1] T.-T. Pham, N. E. Maillot, J.-H. Lim, and J.-P. Chevallet, "Latent semantic fusion model for image retrieval and
annotation," in Proc. of the sixteenth ACM conference on Conference on information and knowledge management (CIKM), Lisbon,
Portugal, 2007, pp. 439-444.

[2] R. Lienhart, S. Romberg, and E. Hörster, "Multilayer pLSA for multimodal image retrieval," in Proc. of the ACM
International Conference on Image and Video Retrieval (CIVR), Island of Santorini, Greece, 2009, pp. 1-8.

[3] J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman, "Discovering object categories in image collections," in Proc.
of the IEEE International Conference on Computer Vision (ICCV), Beijing, P.R.China, 2005, pp. 370- 377.

[4] S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene
categories," in Proc. of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New
York, NY, USA, 2006, pp. 2169-2178.

[5] H. Muller, J. Kalpathy-Cramer, I. Eggel, S. Bedrick, S. ı. Radhouani, B. Bakke, C. E. K. Jr, and W. Hersh, "Overview of the
CLEF 2009 medical image retrieval track," in 10th Workshop of the Cross-Language Evaluation Forum, 2009, pp. 1-11.

[6] "trec_eval: A standard tool used by the TREC community for evaluating an ad hoc retrieval run," in
http://trec.nist.gov/trec_eval/. Washington DC, 2010.

== Links ==

=== Full Text ===
http://publications.hevs.ch/index.php/attachments/single/273

[[internal file]]

=== Sonstige Links ===

Multi-modal Medical Image Retrieval - Versionsgeschichte

Gubachelier: Die Seite wurde neu angelegt: „ == Referenz == Yu Caoa, Henning Müller b, Charles E. Kahn, Jr.c, Ethan Munson: Multi-modal Medical Image Retrieval. Department of Computer Science & En…“