Information

  • Publication Type: Bachelor Thesis
  • Workgroup(s)/Project(s):
  • Date: March 2017
  • Date (to): 8. March 2017
  • Matrikelnummer: 1225540
  • First Supervisor: Eduard Gröller

Abstract

Image segmentation is an important processing step in various applications and crucial in the medical field. When a new segmentation technique is introduced, validation and evaluation are essential for medical image analysis. But the automation of these processes is still not sufficient. Many algorithms have been published but there is still no satisfying way to assess whether an algorithm produces more accurate segmentations than another. More effort is spent on the development of algorithms than on their evaluation and therefore many researchers use the less complex subjective methods. For these techniques multiple experts are needed to visually compare several segmentation results, which is a very time-consuming process. Another way of comparing different results is the supervised evaluation method. Here we need experts, who manually segment reference images, which are used for comparison. As seen in recent researches there is a need for unsupervised methods due to many applications, in which user assistance is infeasible. The aim of this thesis is to provide an environment to visually and objectively evaluate segmentation results in the field of vessel segmentations. Our framework enables the comparison at voxel-level with various visualization techniques and objective measurements. These methods are meant to make the comparison more understandable for users. A subjective evaluation is realized through a comparative visualization by using a two- and three-dimensional comparison of voxels. Another general overview is provided by a maximum-intensity projection, which highlights the vessel structure. As purely objective evaluation technique, various metrics are used, to assure independence from experts or a ground truth. By using these techniques this paper presents an approach for evaluating differences in medical images, which does not rely on a permanent presence of an expert.

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BibTeX

@bachelorsthesis{Gall_2017,
  title =      "Comparison of Vessel Segmentation Techniques",
  author =     "Alexander Gall",
  year =       "2017",
  abstract =   "Image segmentation is an important processing step in
               various applications and crucial in the medical field. When
               a new segmentation technique is introduced, validation and
               evaluation are essential for medical image analysis. But the
               automation of these processes is still not sufficient. Many
               algorithms have been published but there is still no
               satisfying way to assess whether an algorithm produces more
               accurate segmentations than another. More effort is spent on
               the development of algorithms than on their evaluation and
               therefore many researchers use the less complex subjective
               methods. For these techniques multiple experts are needed to
               visually compare several segmentation results, which is a
               very time-consuming process. Another way of comparing
               different results is the supervised evaluation method. Here
               we need experts, who manually segment reference images,
               which are used for comparison. As seen in recent researches
               there is a need for unsupervised methods due to many
               applications, in which user assistance is infeasible. The
               aim of this thesis is to provide an environment to visually
               and objectively evaluate segmentation results in the field
               of vessel segmentations. Our framework enables the
               comparison at voxel-level with various visualization
               techniques and objective measurements. These methods are
               meant to make the comparison more understandable for users.
               A subjective evaluation is realized through a comparative
               visualization by using a two- and three-dimensional
               comparison of voxels. Another general overview is provided
               by a maximum-intensity projection, which highlights the
               vessel structure. As purely objective evaluation technique,
               various metrics are used, to assure independence from
               experts or a ground truth. By using these techniques this
               paper presents an approach for evaluating differences in
               medical images, which does not rely on a permanent presence
               of an expert.",
  month =      mar,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2017/Gall_2017/",
}