Spatially-Variant Morpho-Hessian Filter: Efficient Implementation and Application

  • Authors:
  • Olena Tankyevych;Hugues Talbot;Petr Dokladál;Nicolas Passat

  • Affiliations:
  • Département d'Informatique Gaspard-Monge, Équipe A3SI, ESIEE Paris, Université Paris-Est, Noisy-le-Grand Cedex, France F-93162;Département d'Informatique Gaspard-Monge, Équipe A3SI, ESIEE Paris, Université Paris-Est, Noisy-le-Grand Cedex, France F-93162;Centre de Morphologie Mathématique, Mines-Paristech, Fontainebeau, France F-77300;Université de Strasbourg, LSIIT UMR, Strasbourg, France 7005

  • Venue:
  • ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
  • Year:
  • 2009

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Abstract

Elongated objects are more difficult to filter than more isotropic ones because they locally comprise fewer pixels. For thin linear objects, this problem is compounded because there is only a restricted set of directions that can be used for filtering, and finding this local direction is not a simple problem. In addition, disconnections can easily appear due to noise. In this paper we tackle both issues by combining a linear filter for direction finding and a morphological one for filtering. More specifically, we use the eigen-analysis of the Hessian for detecting thin, linear objects, and a spatially-variant opening or closing for their enhancement and reconnection. We discuss the theory of spatially-variant morphological filters and present an efficient algorithm. The resulting spatially-variant morphological filter is shown to successfully enhance directions in 2D and 3D examples illustrated with a brain blood vessel segmentation problem.