“Nonparametric Local Smoothing” is not image registration

BMC Research Notes

Brian Avants²  Torsten Rohlfing¹ 

[1] Neuroscience Program, SRI International, 333 Ravenswood Avenue, CA 94025, Menlo Park, USA;Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania School of Medicine, PA 19104, Philadelphia, USA

关键词: Accuracy;    Correspondence;    Image registration;   

Others  :  1165311
DOI  :  10.1186/1756-0500-5-610

 received in 2012-05-24, accepted in 2012-09-22,  发布年份 2012

【 摘 要 】

Background

Image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081–2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map.

Results

Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations.

Conclusions

This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities.

【 授权许可】

   
2012 Rohlfing and Avants; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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