Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information
Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2007-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2007/93479 |
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| Summary: | Traditional mutual information (MI) function aligns two
multimodality images with intensity information, lacking spatial
information, so that it usually presents many local maxima that can
lead to inaccurate registration. Our paper proposes an algorithm of
adaptive combination of intensity and gradient field mutual
information (ACMI). Gradient code maps (GCM) are constructed by
coding gradient field information of corresponding original images.
The gradient field MI, calculated from GCMs, can provide
complementary properties to intensity MI. ACMI combines intensity MI
and gradient field MI with a nonlinear weight function, which can
automatically adjust the proportion between two types MI in
combination to improve registration. Experimental results
demonstrate that ACMI outperforms the traditional MI and it is much
less sensitive to reduced resolution or overlap of images. |
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| ISSN: | 1687-4188 1687-4196 |