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Proceedings Paper

Non-rigid contour-to-pixel registration of photographic and quantitative light-induced fluorescence imaging of decalcified teeth
Author(s): Benjamin Berkels; Thomas Deserno; Eva E. Ehrlich; Ulrike B. Fritz; Ekaterina Sirazitdinova; Rosalia Tatano
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Paper Abstract

Quantitative light-induced fluorescence (QLF) is widely used to assess the damage of a tooth due to decalcification. In digital photographs, decalcification appears as white spot lesions, i.e. white spots on the tooth surface. We propose a novel multimodal registration approach for the matching of digital photographs and QLF images of decalcified teeth. The registration is based on the idea of contour-to-pixel matching. Here, the curve, which represents the shape of the tooth, is extracted from the QLF image using a contour segmentation by binarization and morphological processing. This curve is aligned to the photo with a non-rigid variational registration approach. Thus, the registration problem is formulated as minimization problem with an objective function that consists of a data term and a regularizer for the deformation. To construct the data term, the photo is pointwise classified into tooth and non-tooth regions. Then, the signed distance function of the tooth region allows to measure the mismatch between curve and photo. As regularizer a higher order, linear elastic prior is used. The resulting minimization problem is solved numerically using bilinear Finite Elements for the spatial discretization and the Gauss-Newton algorithm. The evaluation is based on 150 image pairs, where an average of 5 teeth have been captured from 32 subjects. All registrations have been confirmed correctly by a dental expert. The contour-to-pixel methods can directly be used in 3D for surface-to-voxel tasks.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840Z (21 March 2016); doi: 10.1117/12.2216250
Show Author Affiliations
Benjamin Berkels, RWTH Aachen Univ. (Germany)
Thomas Deserno, Uniklinik RWTH Aachen Univ. (Germany)
Eva E. Ehrlich, Uniklinik RWTH Aachen Univ. (Germany)
Ulrike B. Fritz, Uniklinik RWTH Aachen Univ. (Germany)
Ekaterina Sirazitdinova, Uniklinik RWTH Aachen Univ. (Germany)
Rosalia Tatano, RWTH Aachen Univ. (Germany)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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