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Proceedings Paper

Integrating user profile in medical CBIR systems to answer perceptual similarity queries
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Paper Abstract

Techniques for Content-Based Image Retrieval (CBIR) have been intensively explored due to the increase in the amount of captured images and the need of fast retrieval of them. The medical field is a specific example that generates a large flow of information, especially digital images employed for diagnosing. One issue that still remains unsolved deals with how to reach the perceptual similarity. That is, to achieve an effective retrieval, one must characterize and quantify the perceptual similarity regarding the specialist in the field. Therefore, the present paper was conceived to fill in this gap creating a consistent support to perform similarity queries over medical images, maintaining the semantics of a given query desired by the user. CBIR systems relying in relevance feedback techniques usually request the users to label relevant images. In this paper, we present a simple but highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The user profiles maintain the settings desired for each user, allowing tuning the similarity assessment, which encompasses dynamically changing the distance function employed through an interactive process. Experiments using computed tomography lung images show that the proposed approach is effective in capturing the users' perception.

Paper Details

Date Published: 4 March 2011
PDF: 12 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630Q (4 March 2011); doi: 10.1117/12.878095
Show Author Affiliations
Pedro H. Bugatti, Univ. de São Paulo (Brazil)
Daniel S. Kaster, Univ. de São Paulo (Brazil)
Univ. Estadual de Londrina (Brazil)
Marcelo Ponciano-Silva, Univ. de São Paulo (Brazil)
Agma J. M. Traina, Univ. de São Paulo (Brazil)
Caetano Traina, Univ. de São Paulo (Brazil)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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