Abstract
Mutual information has been proposed as a criterion for image registration. The criterion is calculated from a two- dimensional grey-scale histogram of the image pair being registered. In this paper we study how sparse sampling can be used to increase speed performance using the registration algorithm of Maes et al. (IEEE Trans Med Imaging 1997; 16: 187- 198) with a focus on registration of MRI-SPET brain images. In particular we investigate how sparse sampling and parameters such as the number of bins used for the grey-scale histograms and smoothing of the data prior to registration affect accuracy and robustness of the registration. The method was validated using both simulated and human data. Our results show that sparse sampling introduced local maxima into the mutual information similarity function when the number of bins used for the histograms was large. To speed up registration while retaining robustness, smoothing of the data prior to registration was used and a coarse to fine subsampling protocol, where the number of bins in the histograms were dependent on the subsampling factor, was employed. For the simulated data, the method was able to recover known transformations with an accuracy of about 1 mm. Using the human data, there were no significant differences in the recovered transformation parameters when the suggested subsampling scheme was used compared with when no subsampling was used, but there was a more than tenfold increase in speed. Our results show that, with the appropriate choice of parameters, the method can accurately register MRI-SPET brain images even when very efficient sampling protocols are used.
| Language | English |
|---|---|
| Pages | 847-856 |
| Number of pages | 10 |
| Journal | European journal of nuclear medicine |
| Volume | 27 |
| Issue number | 7 |
| Publication status | Published - 2000 |
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Keywords
- Image registration
- Mutual information
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Improved efficiency for MRI-SPET registration based on mutual information. / Thurfjell, L.; Lau, Y. H.; Andersson, J. L R; Hutton, B. F.
In: European journal of nuclear medicine, Vol. 27, No. 7, 2000, p. 847-856.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Improved efficiency for MRI-SPET registration based on mutual information
AU - Thurfjell, L.
AU - Lau, Y. H.
AU - Andersson, J. L R
AU - Hutton, B. F.
PY - 2000
Y1 - 2000
N2 - Mutual information has been proposed as a criterion for image registration. The criterion is calculated from a two- dimensional grey-scale histogram of the image pair being registered. In this paper we study how sparse sampling can be used to increase speed performance using the registration algorithm of Maes et al. (IEEE Trans Med Imaging 1997; 16: 187- 198) with a focus on registration of MRI-SPET brain images. In particular we investigate how sparse sampling and parameters such as the number of bins used for the grey-scale histograms and smoothing of the data prior to registration affect accuracy and robustness of the registration. The method was validated using both simulated and human data. Our results show that sparse sampling introduced local maxima into the mutual information similarity function when the number of bins used for the histograms was large. To speed up registration while retaining robustness, smoothing of the data prior to registration was used and a coarse to fine subsampling protocol, where the number of bins in the histograms were dependent on the subsampling factor, was employed. For the simulated data, the method was able to recover known transformations with an accuracy of about 1 mm. Using the human data, there were no significant differences in the recovered transformation parameters when the suggested subsampling scheme was used compared with when no subsampling was used, but there was a more than tenfold increase in speed. Our results show that, with the appropriate choice of parameters, the method can accurately register MRI-SPET brain images even when very efficient sampling protocols are used.
AB - Mutual information has been proposed as a criterion for image registration. The criterion is calculated from a two- dimensional grey-scale histogram of the image pair being registered. In this paper we study how sparse sampling can be used to increase speed performance using the registration algorithm of Maes et al. (IEEE Trans Med Imaging 1997; 16: 187- 198) with a focus on registration of MRI-SPET brain images. In particular we investigate how sparse sampling and parameters such as the number of bins used for the grey-scale histograms and smoothing of the data prior to registration affect accuracy and robustness of the registration. The method was validated using both simulated and human data. Our results show that sparse sampling introduced local maxima into the mutual information similarity function when the number of bins used for the histograms was large. To speed up registration while retaining robustness, smoothing of the data prior to registration was used and a coarse to fine subsampling protocol, where the number of bins in the histograms were dependent on the subsampling factor, was employed. For the simulated data, the method was able to recover known transformations with an accuracy of about 1 mm. Using the human data, there were no significant differences in the recovered transformation parameters when the suggested subsampling scheme was used compared with when no subsampling was used, but there was a more than tenfold increase in speed. Our results show that, with the appropriate choice of parameters, the method can accurately register MRI-SPET brain images even when very efficient sampling protocols are used.
KW - Image registration
KW - Mutual information
UR - http://www.scopus.com/inward/record.url?scp=0033937034&partnerID=8YFLogxK
M3 - Article
VL - 27
SP - 847
EP - 856
JO - European journal of nuclear medicine
T2 - European journal of nuclear medicine
JF - European journal of nuclear medicine
SN - 0340-6997
IS - 7
ER -