It was shown that good WM peak discrimination can be achieved even when there is a large overlap between gray matter (GM) and WM peaks, as is the case with the T2-weighted brain images. Furthermore, the utility of even order derivative analysis in the MRI histogram was demonstrated in. It was shown that this method can reduce the variations in white matter (WM) intensities from 7.5 to 2.5%. The previous studies concerning work on MRI intensity normalization are briefly reviewed as follows.Ī histogram matching method was proposed for correcting the variations in scanner sensitivity due to differences in scanner performance. Therefore, an intensity normalization of MRI scans, which aims at correcting for scanner-dependent variations, is essential for accurate MRI analysis. However, images from different scanners or with different acquisition parameters may have large intensity variations, which greatly affects the results of image analysis. Normalization of the observed image intensities is of crucial importance to explore the disease progression in many clinical studies. The differences in subject positioning between sites or a baseline and a later scan, or protocol can be found, making the interpretation difficult without intensity normalization. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects scanned with different scanner types and scanning parameters.
#Human brain mapping fsl course registration#
Although lacking of a normalized intensity scale of MRI has no direct effect on clinical medical diagnosis by doctors, the situation is complicated by some image post-processing technique, such as automatic segmentation, registration and quantification method, which are highly dependent on the intensity information to achieve favorable results. Magnetic resonance imaging (MRI), as a non-invasive imaging method, has been widely used to study and analyze human brains. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template. We have validated that the method can greatly improve the image analysis performance. The method can normalize scans which were acquired on different MRI units. We have proposed a histogram-based MRI intensity normalization method. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis.
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