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--- other:dsx:x4d:conditional_entropy [2024/07/12 13:56] – created sgranger
+++ other:dsx:x4d:conditional_entropy [2025/05/29 14:32] (current) – wikisysop
@@ Line 1: / Line 1: @@
-====== Conditional_Entropy ======
+====== Conditional Entropy ======
+Conditional Entropy (more specifically cross-conditional entropy) is a specific image metric that can be used by X4D when [[other:dsx:x4d:matching_x-ray_and_drr_images|matching X-Ray and digitally reconstructed radiograph (DRR) images]].
+===== Matching Process =====
-The cross-conditional entropy, //H//, of the DRR image relative to the X-ray image is calculated and minimized by the optimization algorithm <sup>[[#cite_note-1|[1]]]</sup>.
+The cross-conditional entropy, //H//, of the [[other:dsx:dsx_definitions#digitally_reconstructed_radiograph_drr|DRR]] image relative to the X-ray image is calculated and minimized by Wang et al.'s optimization algorithm <sup>[[#cite_note-1|[1]]]</sup>.
-{{X4DConditionalEntropy.png}}
+{{:X4DConditionalEntropy.png}}
-|Parameters                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+===== Parameters =====
-|{{CE_Parameters.png}}\\ \\ Parameters for Conditional Entropy Metric\\ \\ * **View Weighting** This factor is used when evaluating a bone pose by comparing the DRR images to the X-ray images. It is the relative weight of the two views in the final image metric (0.0 = all view 1, 1.0 = all view 2). 0.5 means to weight each view equally.\\ \\ * **Constant DRR Scaling** Whether or not to scale the DRRs for each view by a constant value for all iterations of an optimization. If false, each DRR will be scaled so that its maximum value is equal to the DRR image scale value ([[Other:DSX:X4D:X-ray_Windows|//X-ray/DRR Settings//]] widget). If true, a single scale value will be calculated during the first iteration and be used to scale the DRRs in all subsequent iterations.  |
-  - [[#cite_ref-1|↑]] Wang F, Vemuri B, Rao M, Chen Y (2003). Cumulative residual entropy, a new measure of information & its application to image alignment. Proceedings of the Ninth IEEE International Conference on Computer Vision, October, 2003. DOI: 10.1109/ICCV.2003.1238395.
+{{:CE_Parameters.png}}
+^ Parameter ^ Description ^
+| View Weighting | This factor is used when evaluating a bone pose by comparing the DRR images to the X-ray images. It is the relative weight of the two views in the final image metric, so 0.0 indicates all View 1, 1.0 indicates all View 2, and 0.5 indicates to weight each view equally. |
+| Constant DRR Scaling | Indicates whether or not to scale the DRRs for each view by a constant value for all iterations of an optimization. If false, each DRR will be scaled so that its maximum value is equal to the DRR image scale value ([[Other:DSX:X4D:X-ray_Windows|X-ray/DRR Settings]] widget). If true, a single scale value will be calculated during the first iteration and be used to scale the DRRs in all subsequent iterations. |
+===== See Also =====
+[[other:dsx:x4d:pixel_difference|Pixel Difference]] is another image metric that can be used by X4D.
+[[#cite_ref-1|↑]] Wang F, Vemuri B, Rao M, Chen Y (2003). Cumulative residual entropy, a new measure of information & its application to image alignment. Proceedings of the Ninth IEEE International Conference on Computer Vision, October, 2003. DOI: 10.1109/ICCV.2003.1238395.