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Computational Intelligence in Expensive Optimization Problems pp 671–700Cite as

A Distributed Evolutionary Approach to Subtraction Radiography

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Part of the book series: Adaptation Learning and Optimization ((ALO,volume 2))

Abstract

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.

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Author information

Authors and Affiliations

  1. National University, Carrera 45 N 26-85, Bogotá, Colombia

    Gabriel Mañana Guichón & Eduardo Romero Castro

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  1. Gabriel Mañana Guichón
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  2. Eduardo Romero Castro
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Editors and Affiliations

  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-Ku,, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2, Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

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Guichón, G.M., Castro, E.R. (2010). A Distributed Evolutionary Approach to Subtraction Radiography. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Expensive Optimization Problems. Adaptation Learning and Optimization, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10701-6_25

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  • DOI: https://doi.org/10.1007/978-3-642-10701-6_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10700-9

  • Online ISBN: 978-3-642-10701-6

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