Name
Abstract | ||
|---|---|---|
Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. The influence of standardization on these tasks has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the role of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patrec.2009.09.010 | Pattern Recognition Letters |
Keywords | Field | DocType |
intensity standardization,image registration,quantitative validation,inhomogeneity correction,non-standardness,different image,clinical mr image,mr image,image display,clinical mr image registration,medical image registration,image gray scale,standard image intensity scale,image segmentation | Computer vision,Similitude,Signal intensity,Pattern recognition,Image processing,Image segmentation,Artificial intelligence,Standardization,Grayscale,Mathematics,Image registration,Image display | Journal |
Volume | Issue | ISSN |
31 | 4 | Pattern Recognition Letters |
Citations | PageRank | References |
12 | 0.70 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulaş Bağcı | 1 | 77 | 9.70 |
| Jayaram K. Udupa | 2 | 2481 | 322.29 |
| Li Bai | 3 | 789 | 50.45 |