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Choice of the Hough transform for image registration

Image registration algorithms should be robust against partly erroneous and inconsistent data. The evidence accumulation mechanism known as the Hough Transform (HT) finds the solution indicated by the largest consistent subset of the data. The important case of feature-based registration under the simplified affine transformation, that is, translation, rotation and isotropic scaling, can be easily stated in the terms of HT. Until recently, the use of HT in the considered application was prohibited by excessive computational requirements, but the development of the hardware permanently relieves these limitations. Three versions of the HT, both in the crisp and fuzzy version, were examined against the test images: the Generalized HT (GHT), the Modified Iterated HT (MIHT), and the version called here the Direct Accumulation HT (DAHT), known also as GIPSC on the grounds of photogrammetry. The results indicate that the fuzzy DAHT is robust for over 50% of errors in data, fuzzy GHT up to nearly 30%, and that all the crisp versions as well as the fuzzy MIHT are fragile at least for some examples. The practical applicability of the DAHT and GHT is shown for hierarchical registration of simulation and portal images used in quality assessment of oncological radiotherapy.