Research Article Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics Kelvin Leung, 1,2 Alexandre Cunha, 3 A. W. Toga, 4 and D. Stott Parker 2 1 Intel Corporation, 3600 Julliette Ln., Mail Stop SC12-301, Santa Clara, CA 95054, USA 2 UCLA Computer Science Department, Los Angeles, CA 90095-1596, USA 3 Caltech Center for Advanced Computing Research (CACR), Pasadena, CA 91125, USA 4 USC Laboratory of Neuroimaging (LONI), Los Angeles, CA 90007, USA Correspondence should be addressed to Kelvin Leung; kelvin.t.leung@intel.com 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. his 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 oten 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 diferent image processing results with a number of diferent metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics ofers a variety of potential beneits for many image processing problems, including improved robustness and validation. 1. Introduction Every year many articles are published in the area of biomedi- cal image registration that introduce new metrics for biomed- ical images, covering both distance/diference measures and similarity measures. here are many reasons for this interest in metrics. However, the abundance of methods creates a basic dilemma for practitioners seeking high-performance imaging systems: which metric should be used? his paper reports on an efort spanning ive years at UCLA, studying this question, and developing schemes that use multiple methods and multiple evaluation metrics to obtain better image processing results. In much the same way that ensemble methods yield better results in data mining, this efort explored sotware combinations of metrics that yielded improved methods for registration in neuroimaging. In this paper we consider two families of image similarity metrics: intensity-based metrics (metrics of the intensity or luminosity values of voxels) and statistical metrics (metrics of their distributions). here are at least three reasons why use of multiple metrics can be important in image processing as follows. (i) Metrics are performance measures, so awareness of them is a prerequisite for good performance. Although it is common to commit ab initio to a single registration algorithm and metric, algorithms and metrics difer signiicantly, and choices among them can have important consequences. (ii) here are inherent limits to image processing per- formance. From this perspective, image processing methods are little more than optimizers that rest on assumptions about prior distributions of images and validation as experimental veriication of these distributions. However, if metric values can be treated as samples of prior distributions on performance measures, we can mitigate some of these limits. (iii) he key point of this paper is that the results of diferent image processing algorithms and parameter settings can be evaluated under multiple metrics, and the metric values can then be analyzed with data mining to identify the best results. he tracking of metric value results permits investigation of which image processing methods give better results for Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2014, Article ID 383465, 7 pages http://dx.doi.org/10.1155/2014/383465