Paper
15 May 2003 The interplay between intensity standardization and field inhomogeneity correction in MR image processing
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Abstract
Image intensity standardization is a recently developed post-processing method designed for correcting acquisition-to-acquisition signal intensity variations inherent in MR images. Inhomogeneity correction is a method used to suppress the low frequency background non-uniformities of the image domain that exist in MR images. Both these procedures have important implications for MR image analysis. The effects of these post-processing operations on improvement of image quality in isolation has been well documented [1-11]. However, the combined effects of these two processes on MR images and how the processes influence each other have not been studied thus far. In this paper, we evaluate the effect of inhomogeneity correction followed by standardization on MR images and vice-versa in order to determine the best sequence to follow for enhancing image quality. Our results indicate that improved standardization can be achieved by preceding it with inhomogeneity correction. There is no statistically significant difference in image quality obtained between the results of standardization followed by correction and that of correction followed by standardization from the perspective of inhomogeneity correction. The correction operation was found to bias the effect of standardization. We demonstrated this bias both qualitatively and quantitatively. Standardization on the other hand did not influence the correction operation. It was also found that longer sequences of repeated correction and standardization did not considerably improve image quality.
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Anant Madabhushi and Jayaram K. Udupa "The interplay between intensity standardization and field inhomogeneity correction in MR image processing", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481345
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Cited by 3 scholarly publications.
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KEYWORDS
Image quality standards

Magnetic resonance imaging

Image quality

Image processing

Tissues

Image segmentation

Image analysis

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