A multiresolution diffused expectation-maximization algorithm for medical image segmentation

  • Authors:
  • Giuseppe Boccignone;Paolo Napoletano;Vittorio Caggiano;Mario Ferraro

  • Affiliations:
  • Natural Computation Lab, DIIIE-Universitá di Salerno, via Ponte Don Melillo, 1, 84084 Fisciano (SA), Italy;Natural Computation Lab, DIIIE-Universitá di Salerno, via Ponte Don Melillo, 1, 84084 Fisciano (SA), Italy;Dipartimento di Informatica e Sistemistica, Universitá di Napoli Federico II, via Claudio, 21, 80125 Napoli, Italy;Dipartimento di Fisica Sperimentale, Universitá di Torino, via P. Giuria, 1, 10100 Torino, Italy

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2007

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Abstract

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.