TY - GEN
T1 - Quadrature-based image registration method using mutual information
AU - Fookes, C.
AU - Maeder, A.
PY - 2004/12/1
Y1 - 2004/12/1
N2 - Mutual information (MI) is a popular entropy-based similarity measure used in the medical imaging field for multi-modal registration. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximize the MI between two images. A common implementation of MI involves the use of Parzen windows. This process generally requires two samples of image intensities: one to estimate the underlying intensity distributions and the second to estimate the entropy. This paper presents a novel gradient-based registration algorithm (MIGH) which uses Gauss-Hermite quadrature to estimate the image entropies. The use of this technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities. With this technique, it is possible to achieve similar results and registration accuracy when compared to current Parzen-based MI techniques. These results are achieved using half the previously required sample sizes and also with an improvement in algorithm complexity.
AB - Mutual information (MI) is a popular entropy-based similarity measure used in the medical imaging field for multi-modal registration. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximize the MI between two images. A common implementation of MI involves the use of Parzen windows. This process generally requires two samples of image intensities: one to estimate the underlying intensity distributions and the second to estimate the entropy. This paper presents a novel gradient-based registration algorithm (MIGH) which uses Gauss-Hermite quadrature to estimate the image entropies. The use of this technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities. With this technique, it is possible to achieve similar results and registration accuracy when compared to current Parzen-based MI techniques. These results are achieved using half the previously required sample sizes and also with an improvement in algorithm complexity.
UR - http://www.scopus.com/inward/record.url?scp=17144425046&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2004.1398641
DO - 10.1109/ISBI.2004.1398641
M3 - Conference contribution
AN - SCOPUS:17144425046
SN - 0780383885
T3 - 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
SP - 728
EP - 731
BT - 2004 2nd IEEE International Symposium on Biomedical Imaging
T2 - 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Y2 - 15 April 2004 through 18 April 2004
ER -