A unifying approach to registration, segmentation, and intensity correction

K. M. Pohl, F. Fisher, J. J. Levitt aand M. E. Shenton, R. Kikinis, W. E. L. Grimson, W. M. Wells
Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2005
Volume 3749, Pages 310-318
2005

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Abstract

We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images.We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems.

Reference

Pohl KM, Fisher F, aand M E Shenton JJL, Kikinis R, Grimson WEL, Wells WM. A unifying approach to registration, segmentation, and intensity correction. In J Duncan, G Gerig, eds., Eighth International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2005, volume 3749 of Lecture Notes in Computer Science. Palm Springs, CA, USA: Springer Verlag, Berlin Heidelberg 2005, 2005;310-318.

Grants

NIH/NIMH 2K02 MH01110, VA Merit Award(MES) 2000-2008, REAP, NIH RO1 MH50747, R01-NS051826-01, NIH P41 RR13218, U24 RR021382, U54 EB005149

Research area

segmentation
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