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Segmentation-Assisted Registration for Brain MR Images

  • Jundong Liu
Chapter

Abstract

Tracking brain morphological changes in magnetic resonance images (MRIs) requires high accurate segmentation or registration of brain structures. In this chapter, we propose a robust solution for this problem, based on a unified variation framework where prior segmentation information can be seamlessly integrated into a nonrigid registration procedure. Under this framework, in addition to the force arising from the similarity minimization in seeking for detailed correspondence, atlas contours provide an extra guidance to assist the alignment procedure in achieving a more meaningful, stable, and noise-tolerant result. Sum of local correlation (LC) is used as the underlying similarity metric. Our approach can very well handle intensity scaling, contrast reversal, as well as image noise. Experimental results on 2D/3D synthetic and real data demonstrate the improvement made by our algorithm.

Keywords

Local Correlation Nonrigid Registration Normal Pressure Hydrocephalus Fixed Image Intensity Scaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceOhio UniversityAthensUSA

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