Css Slider 2.1 Registration Key 494

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Css Slider 2.1 Registration Key 494

finally, we compared our proposed algorithm with the method by balakrishnan et al.[ 7 ] mentioned in the background section. in this paper, they proposed a pixel-level registration algorithm which is based on a patch-based similarity measurement. we also used similar pre-processing steps on the tissue slides of 20 rois to increase the robustness of our algorithm. since, it is not possible to measure similarity at pixel level for an entire whole tissue slide, we randomly sampled patches of size(5 × 5, 3 × 3, and 1 × 1) from the tissue slides to measure similarity. also, since the algorithm does not use any pre-selection mechanism for the sift key points, we sampled sift key points randomly for registration. similar to our method, the error of registration is measured using a small patch size using the same pre-processing techniques as mentioned in the background section. note that the size of the sift key points used for both methods is the same. we also measured the execution time for these methods.

the results of this comparison are provided in the table. as can be seen, our proposed algorithm produced a more robust estimation of the transformation compared to the method by balakrishnan et al.[ 7 ]. there is a clear error between our method and their algorithm for every patch size. also, we see that the method by balakrishnan et al.

the proposed registration algorithm consists of a few steps:

• detect the roi and combine roi regions into one unique roi in consecutive image slices using the active contour algorithm by ken-vellum and hildreth[ 15 ].
• perform linear (affine) registration on the roi region using a b-spline transformation technique[ 26, 27 ].
• check if the deformation is severe.
• if so, increase the number of iterations in the registration process or change the algorithm to be used for refining the deformation. otherwise, check if the roi in consecutive image slices can be aligned with a result closer to the expected result.

the same 20 sift key points that we used for comparison with the proposed algorithm are also used by them. however, in the case of their algorithm, instead of using the same sift key points, we used a different key point set that is randomly selected for each roi. for this comparison, we used the same pre-processing steps as they applied to increase the robustness of their algorithm. we performed a rigid transformation on the roi manually and registered the three channels, hematoxylin, dapi and the luminance channel using their method. we also applied the deformation fields resulting from this algorithm to the tissue slides to find their registration results on the hematoxylin and dapi channel to compare with ours. also, in the same way as they do, we applied the hematoxylin and dapi channels to their deformation field and averaged the results to measure the accuracy of their algorithm. since, our proposed algorithm was developed with a multi-scale strategy that utilizes a large patch region to robustly locate rois, we also compare our results with the proposed algorithm for a patch size of 3 × 3 and 5 × 5. the results of this comparison with their algorithm are shown in fig. 9. as can be seen, our algorithm performed robustly with an average of ncc of 0.69 when the patch size was 5 × 5. although our proposed method does not perform as well as their algorithm at an average ncc of 0.81 using the patch size of 3 × 3, it still performs well as most of the widely used rigid registration algorithms in the literature (such as anzanello et al.[ 4 ]and wang and chen[ 6 ]) on this dataset. the comparison of the results is provided in the table. also, the performance of their algorithm degrades when the patch size is reduced to 3 × 3 which agrees with the results of our proposed algorithm on this dataset. overall, we support the previous assumption that the multi-scale strategy is a key factor that makes their algorithm robust to major deformation that may exist outside of the roi.
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