SPECIAL SECTION ON MODEL-BASED MEDICAL IMAGE PROCESSING AND ANALYSIS Probabilistic Models

Statistical framework for model-based image retrieval in medical applications

[+] Author Affiliations
Daniel Keysers, Jo¨rg Dahmen, Hermann Ney

RWTH Aachen—University of Technology, Lehrstuhl fu¨r Informatik VI, Computer Science Department, D-52056 Aachen, Germany E-mail: keysers@cs.rwth-aachen.de

Berthold B. Wein

RWTH Aachen–University of Technology, Department of Diagnostic Radiology, Medical Faculty, D-52057 Aachen, Germany

Thomas M. Lehmann

RWTH Aachen–University of Technology, Institute of Medical Informatics, Medical Faculty, D-52057 Aachen, Germany

J. Electron. Imaging. 12(1), 59-68 (Jan 01, 2003). doi:10.1117/1.1525790
History: Received May 1, 2001; Revised Oct. 16, 2002; Accepted Jun. 1, 2002; Online January 29, 2003
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Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to prototypes; extraction and query-dependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, anatomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special emphasis is placed on invariance, employing a probabilistic model of variability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this six-class problem is an excellent result, taking into account the difficulty of the task. The computed posterior probabilities are furthermore used in the subsequent steps of the retrieval process. © 2003 SPIE and IS&T.

© 2003 SPIE and IS&T

Citation

Daniel Keysers ; Jo¨rg Dahmen ; Hermann Ney ; Berthold B. Wein and Thomas M. Lehmann
"Statistical framework for model-based image retrieval in medical applications", J. Electron. Imaging. 12(1), 59-68 (Jan 01, 2003). ; http://dx.doi.org/10.1117/1.1525790


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