Adaptive Vision System for Segmentation of Echographic Medical Images Based on a Modified Mumford-Shah Functional

  • Dimitris K. Iakovidis
  • Michalis A. Savelonas
  • Dimitris Maroulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)


This paper presents a novel adaptive vision system for accurate segmentation of tissue structures in echographic medical images. The proposed vision system incorporates a level-set deformable model based on a modified Mumford-Shah functional, which is estimated over sparse foreground and background regions in the image. This functional is designed so that it copes with the intensity inhomogeneity that characterizes echographic medical images. Moreover, a parameter tuning mechanism has been considered for the adaptation of the deformable model parameters. Experiments were conducted over a range of echographic images displaying abnormal structures of the breast and of the thyroid gland. The results show that the proposed adaptive vision system stands as an efficient, effective and nearly objective tool for segmentation of echographic images.


Genetic Algorithm Thyroid Nodule Parameter Tuning Active Contour Deformable Model 
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 2007

Authors and Affiliations

  • Dimitris K. Iakovidis
    • 1
  • Michalis A. Savelonas
    • 1
  • Dimitris Maroulis
    • 1
  1. 1.Dept. of Informatics and Telecommunications, University of Athens, Panepistimioupolis, 15784, AthensGreece

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