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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fookes, C. Maeder, A. |
| Copyright Year | 2004 |
| Description | Author affiliation: Sch. of Electr. & Electron. Syst. Eng., Queensland Univ. of Technol., Brisbane, Qld., Australia (Fookes, C.; Maeder, A.) |
| Abstract | Mutual information (MI) is a popular entropy-based similarity measure used in the medical imaging field for multimodal 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. |
| Starting Page | 728 |
| Ending Page | 731 |
| File Size | 543873 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780383885 |
| DOI | 10.1109/ISBI.2004.1398641 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-04-18 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image registration Mutual information Entropy Gaussian processes Biomedical imaging Density functional theory Biomedical engineering Systems engineering and theory Australia Electric variables measurement |
| Content Type | Text |
| Resource Type | Article |
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