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Fuzzy Digital Topology and Geometry and Their Applications to Medical Imaging

  • Punam K. Saha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

The primary end-goal of most medical imaging research program is to collect information about function and physiology of internal human organs or tissues through a variety of in vivo or ex vivo imaging techniques. Often, medical imaging techniques suffer from limited spatial and temporal resolution, noise, background-inhomogeneity, and other artifacts leading to fuzzy representations of target objects in acquired images. Digital topology and geometry play important roles in medical image processing either by expanding the scope of target information or by providing a strong theoretical foundation to a process enhancing its stability, fidelity, and efficiency. The notions of digital topology and geometry are often intertwined in medical imaging applications and sometime it is difficult to draw a dividing line between them. This paper presents recent advancements and overviews of theory and computation of several fuzzy digital topologic and geometric approaches and describe their applications to medical imaging. More specifically, this paper discusses topics related to three-dimensional simple points, local topological parameters, fuzzy skeletonization, characterization of local structures, and their applications to research and clinical studies.

Keywords

Fuzzy subset digital imaging topology geometry distance transform simple point skeletonization topological classification medical imaging 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Punam K. Saha
    • 1
  1. 1.Department of Electrical and Computer Engineering, Department of RadiologyUniversity of IowaIowa CityUSA

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