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Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps

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Computational Intelligence Paradigms in Advanced Pattern Classification

Part of the book series: Studies in Computational Intelligence ((SCI,volume 386))

Abstract

This chapter describes basis of brain perfusion computed tomography imaging (CTP) and computer based method for classification and visualization perfusion abnormalities. The solution proposed by author – perfusion abnormality detection measure and description (DMD) system – is consisted of the unified algorithm for detection of asymmetry in CBF and CBV perfusion maps, the image registration algorithm based on adaptation of a free form deformation model and the description / diagnosis algorithm. The DMD system was validated on set of 37 triplets of medical images acquired from 30 different adult patients (man and woman) with suspicious of ischemia / stroke. 77.0% of tested maps were rightly classified and the visible lesions were detected and described identically to radiologist diagnosis. In this chapter the author presents also portable augmented reality interface for visualization of medical data capable to render not only perfusion CT data but also volumetric images in real time that can be run on off – the – shelf computer.

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Authors and Affiliations

  1. Institute of Computer Science and Computer Methods, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084, Krakow, Poland

    Tomasz Hachaj

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  1. Tomasz Hachaj
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Correspondence to Tomasz Hachaj .

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Editors and Affiliations

  1. , Institute of Automatics, AGH University of Science and Technology, 30 Mickiewicza Ave, Krakow, 30-059, Poland

    Marek R. Ogiela

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

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Hachaj, T. (2012). Pattern Classification Methods for Analysis and Visualization of Brain Perfusion CT Maps. In: Ogiela, M., Jain, L. (eds) Computational Intelligence Paradigms in Advanced Pattern Classification. Studies in Computational Intelligence, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24049-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-24049-2_8

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  • Online ISBN: 978-3-642-24049-2

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