Central European Journal of Physics

, Volume 8, Issue 5, pp 689–698

A Q-Ising model application for linear-time image segmentation

Research Article

DOI: 10.2478/s11534-009-0165-y

Cite this article as:
Bentrem, F.W. centr.eur.j.phys. (2010) 8: 689. doi:10.2478/s11534-009-0165-y


A computational method is presented which efficiently segments digital grayscale images by directly applying the Q-state Ising (or Potts) model. Since the Potts model was first proposed in 1952, physicists have studied lattice models to gain deep insights into magnetism and other disordered systems. For some time, researchers have realized that digital images may be modeled in much the same way as these physical systems (i.e., as a square lattice of numerical values). A major drawback in using Potts model methods for image segmentation is that, with conventional methods, it processes in exponential time. Advances have been made via certain approximations to reduce the segmentation process to power-law time. However, in many applications (such as for sonar imagery), real-time processing requires much greater efficiency. This article contains a description of an energy minimization technique that applies four Potts (Q-Ising) models directly to the image and processes in linear time. The result is analogous to partitioning the system into regions of four classes of magnetism. This direct Potts segmentation technique is demonstrated on photographic, medical, and acoustic images.


image segmentation Potts model 

Copyright information

© © Versita Warsaw and Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Marine Geosciences Division, Naval Research LaboratoryStennis Space CenterMississippiUSA
  2. 2.Naval Research LaboratoryCenter for Bio/Molecular Science and EngineeringWashington, DCUSA
  3. 3.Department of Physics and Engineering PhysicsTulane UniversityNew OrleansUSA

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