Buhmann, Joachim M.
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Automatic, defect tolerant registration of transmission electron microscopy (TEM) images poses an important and challenging problem for biomedical image analysis, e.g. in computational neuroanatomy. In this paper we demonstrate a fully automatic stitching and distortion correction method for TEM images and propose a probabilistic approach for image registration that implicitly detects image defects due to sample preparation and image acquisition. The approach uses a polynomial kernel expansion to estimate a non-linear image transformation based on intensities and spatial features. Corresponding points in the images are not determined beforehand, but they are estimated via an EM-algorithm during the registration process which is preferable in the case of (noisy) TEM images. Our registration model is successfully applied to two large image stacks of serial section TEM images acquired from brain tissue samples in a computational neuroanatomy project and shows significant improvement over existing image registration methods on these large datasets Show more
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Journal / seriesTechnical report / Computer Science Department, ETH Zürich
PublisherEidgenössische Technische Hochschule Zürich, Institute of Computational Science
SubjectCOMPUTER VISION + SCENE UNDERSTANDING (ARTIFICIAL INTELLIGENCE); COMPUTERVISION (KÜNSTLICHE INTELLIGENZ); PROGRAMS AND ALGORITHMS FOR THE SOLUTION OF SPECIAL PROBLEMS; PROGRAMME UND ALGORITHMEN ZUR LÖSUNG SPEZIELLER PROBLEME
Organisational unit02150 - Dep. Informatik / Dep. of Computer Science
NotesTechnical Reports D-INFK.
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