Morphons: Paint on Priors and Elastic Canvas for Segmentation and Registration

  • Hans Knutsson
  • Mats Andersson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


This paper presents a new robust approach for registration and segmentation. Segmentation as well as registration is attained by morphing of an N-dimensional model, the Morphon, onto the Ndimensional data. The approach is general and can, in fact, be said to encompass much of the deformable model ideas that have evolved over the years. However, in contrast to commonly used models, a distinguishing feature of the Morphon approach is that it allows an intuitive interface for specifying prior information, hence the expression paint on priors. In this way it is simple to design Morphons for specific situations.

The priors determine the behavior of the Morphon and can be seen as local data interpreters and response generators. There are three different kinds of priors: – material parameter fields (elasticity, viscosity, anisotropy etc.), – context fields (brightness, hue, scale, phase, anisotropy, certainty etc.) and – global programs (filter banks, estimation procedures, adaptive mechanisms etc.).

The morphing is performed using a dense displacement field. The field is updated iteratively until a stop criterion is met. Both the material parameter and context fields are addressed via the present displacement field. In each iteration the neighborhood operators are applied, using both data and the displaced parameter fields, and an incremental displacement field is computed.

An example of the performance is given using a 2D ultrasound heart image sequence where the purpose is to segment the heart wall. This is a difficult task even for trained specialists yet the Morphon generated segmentation is highly robust. Further it is demonstrated how the Morphon approach can be used to register the individual images. This is accomplished by first finding the displacement field that aligns the morphon model with the heart wall structure in each image separately and then using the displacement field differences to align the images.


Displacement Field Ultrasound Image Coarse Scale Deformable Model Incremental Displacement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hans Knutsson
    • 1
  • Mats Andersson
    • 1
  1. 1.Medical Informatics, Dept. of Biomedical Engineering & Center for Medical Image Science and and VisualizationLinköping UniversityLinköpingSweden

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