A Distributed Evolutionary Approach to Subtraction Radiography

  • Gabriel Mañana Guichón
  • Eduardo Romero Castro
Part of the Adaptation Learning and Optimization book series (ALO, volume 2)


Automatic image registration is a fundamental task in medical image processing, and significant advances have occurred in the last decade. However, one major problem with advanced registration techniques is their high computational cost. Due to this restraint, these methods have found limited application to clinical situations where real time or near real time execution is required, e.g., intraoperative imaging, or high volumes of data need to be processed periodically. High performance in image registration can be achieved by reduction in data and search spaces. However, to obtain a significant increase in performance, these approaches must be complemented with parallel processing. Parallel processing is associated with expensive supercomputers and computer clusters that are unaffordable for most public medical institutions. This chapter will describe how to take advantage of an existing computational infrastructure and achieve high performance image registration in a practical and affordable way. More specifically, it will outline the implementation of a fast and robust Internet subtraction service, using a distributed evolutionary algorithm and a service-oriented architecture.


Evolutionary Algorithm Differential Evolution Image Registration Projective Transformation Joint Probability Density Function 
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 2010

Authors and Affiliations

  • Gabriel Mañana Guichón
    • 1
  • Eduardo Romero Castro
    • 1
  1. 1.National UniversityBogotáColombia

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