Atlas Based Intensity Transformation of Brain MR Images
Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images suffer from wide variability in intensity contrasts due to scanner calibration and pulse sequence variations. Most MR image processing tasks use intensities as the principal feature and therefore the results can vary widely according to the actual tissue intensity contrast. Since it is difficult to control the MR scanner acquisition protocols in multi-scanner cross-sectional studies, results achieved using image processing tools are often difficult to compare in such studies. Similar issues can happen in longitudinal studies, as scanners undergo upgrades or improvements in pulse sequences, leading to new imaging sequences. We propose a novel probabilistic model to transform image contrasts by matching patches of a subject image to a set of patches from a multi-contrast atlas. Although the transformed images are not for diagnostic purpose, the use of such contrast transforms is shown for two applications, (a) to improve segmentation consistency across scanners and pulse sequences, (b) to improve registration accuracy between multi-contrast image pairs by transforming the subject image to the contrast of the reference image and then registering the transformed subject image to the reference image. Contrary to previous intensity transformation methods, our technique does not need any information about landmarks, pulse sequence parameters or imaging equations. It is shown to provide more consistent segmentation across scanners compared to state-of-the-art methods.
Keywordsmagnetic resonance imaging (MRI) intensity transformation intensity normalization histogram matching brain
Unable to display preview. Download preview PDF.
- 8.Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Conf. on Comp. Graphics and Interactive Techniques (SIGGRAPH), pp. 327–340 (2001)Google Scholar
- 9.Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K., Niethammer, M.: Registration for correlative microscopy using image analogies. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 296–306. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 11.Friedman, L., Stern, H., Brown, G.G., Mathalon, D.H., Turner, J., Glover, G.H., Gollub, R.L., Lauriello, J., Lim, K.O., Cannon, T., Greve, D.N., Bockholt, H.J., Belger, A., Mueller, B., Doty, M.J., He, J., Wells, W., Smyth, P., Pieper, S., Kim, S., Kubicki, M., Vangel, M., Potkin, S.G.: Test-Retest and Between-Site Reliability in a Multicenter fMRI Study. Human Brain Mapping 29(8), 958–972 (2008)CrossRefGoogle Scholar
- 13.Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: IEEE Intl. Conf. on Comp. Vision, pp. 2272–2279 (2009)Google Scholar