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Multiple model estimation for the detection of curvilinear segments in medical X-ray images using sparse-plus-dense-RANSAC

Authors
Publisher
IEEE
Publication Date
Disciplines
  • Medicine

Abstract

Multiple Model Estimation for the Detection of Curvilinear Segments in Medical X-ray Images Using Sparse-plus-dense-RANSAC Multiple model estimation for the detection of curvilinear segments in medical X-ray images using sparse-plus-dense-RANSAC Chrysi Papalazarou Univ. of Technol. Eindhoven Eindhoven, the Netherlands Peter M. J. Rongen Philips Healthcare Best, the Netherlands Peter H. N. de With CycloMedia / Univ. Technol. Eindhoven, the Netherlands Abstract In this paper, we build on the RANSAC method to de- tect multiple instances of objects in an image, where the objects are modeled as curvilinear segments with dis- tinct endpoints. Our approach differs from previously presented work in that it incorporates soft constraints, based on a dense image representation, that guide the estimation process in every step. This enables (1) bet- ter correspondence with image content, (2) explicit end- point detection and (3) a reduction in the number of it- erations required for accurate estimation. In the case of curvilinear objects examined in this paper, these con- straints are formulated as binary image labels, where the estimation proved to be robust to mislabeling, e.g. in case of intersections. Results for both synthetic and real data from medical X-ray images show the improve- ment from incorporating soft image-based constraints. 1. Introduction In this paper, we study model estimation, motivated by the problem of object detection in medical X-ray imaging. Many surgical instruments present in medi- cal images can be described by simple one-dimensional models, for example needles, catheters, guidewires, etc. By making a sparse representation of the image in terms of interest points, we can create the input points for the model fitting, while the original dense data remain available. In many cases, an unknown number of ob- jects can be present in the image, while overlapping ob- jects may make it difficult to assign a model to points at the crossings. Finally, it is not suf

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