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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 383465, 7 pages
Research Article

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

1Intel Corporation, 3600 Julliette Ln., Mail Stop SC12-301, Santa Clara, CA 95054, USA
2UCLA Computer Science Department, Los Angeles, CA 90095-1596, USA
3Caltech Center for Advanced Computing Research (CACR), Pasadena, CA 91125, USA
4USC Laboratory of Neuroimaging (LONI), Los Angeles, CA 90007, USA

Received 7 May 2013; Revised 26 November 2013; Accepted 26 November 2013; Published 5 February 2014

Academic Editor: Facundo Ballester

Copyright © 2014 Kelvin Leung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.