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

Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Augmented Reality Cerebral Blood Volume Brain Perfusion Compute Tomo Image Registration Algorithm 
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 Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Computer Science and Computer MethodsPedagogical University of KrakowKrakowPoland

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