Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics
Abstract
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.
Additional Information
© 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. Received 7 May 2013; Revised 26 November 2013; Accepted 26 November 2013; Published 5 February 2014. Academic Editor: Facundo Ballester. The authors thank reviewers for their valuable comments. This work was supported by NIH Grant 1U54RR021813 (Center for Computational Biology). The authors declare that there is no conflict of interests.Attached Files
Published - 383465.pdf
Files
Name | Size | Download all |
---|---|---|
md5:d920b59245e38ac639d776bbca7f7ab8
|
2.1 MB | Preview Download |
Additional details
- PMCID
- PMC3932725
- Eprint ID
- 44179
- Resolver ID
- CaltechAUTHORS:20140306-102702186
- 1U54RR021813
- NIH
- Created
-
2014-03-06Created from EPrint's datestamp field
- Updated
-
2021-11-10Created from EPrint's last_modified field