Analysis of 3D and 4D Images of Organisms in Embryogenesis

Information

• Publication Type: PhD-Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: August 2015
• Date (Start): 1. September 2011
• Date (End): 24. August 2015
• Second Supervisor: Prof. RNDr. Karol Mikula
• 1st Reviewer: Prof. Ing. Miloš Šrámek
• 2nd Reviewer: Doc. RNDr. Peter Frolkovic
• Rigorosum: 24. August 2015
• First Supervisor: Prof. RNDr. Daniel Ševcovi

Abstract

In this work, we present a few modifications to the state-of-the-art algorithms, as well as several novel approaches, related to the detection of cells in biological image processing.

We start by explanation of a PDE-based image processing evolution called FBLSCD and study its properties. We then define a fully automatic way of finding the stop time for this evolution. Afterwards, we try to see the FBLSCD as a morphological grayscale erosion, and we formulate a novel cell detection algorithm, called LSOpen, as an intersection of PDE-based and morphological image processing schools.

Then, we discuss the best ways of inspecting cell detection results, i.e. cell identifiers. We try to quantitatively benchmark various cell detection methods by the relative amount of false positives, false negatives and multiply-detected centers yielded. We will observe that comparing cell detection results in a binary fashion is insufficient, therefore we are going to utilize the concept of distance function.

Motivated by this need for robust cell detection result comparison, we analyze commonly-used methods for computing the distance function and afterwards we formulate a novel algorithm. This one has complexity O(n log2 n) and it yields Euclidean distance. In addition to that, we introduce a modification to this algorithm, enabling it to work also in maze-like, wall- and corner-containing, environments.

This modification relies on the line rasterization algorithm. We perform various experiments to study and compare distance function methods. Results illustrate the viability of newly-proposed method.

Further, a software for the comparing and inspecting cell detection results, SliceViewer, is specified, designed, implemented and tested.

In the end, quantitative experiments are discussed, validating the above-mentioned novelties.

Additional Files and Images

Weblinks

No further information available.

BibTeX

@phdthesis{Smisek_Michal_A3D,
  title =      "Analysis of 3D and 4D Images of Organisms in Embryogenesis",
  author =     "Michal Sm\'{i}\v{s}ek",
  year =       "2015",
  abstract =   "In this work, we present a few modifications to the
               state-of-the-art algorithms, as well as several novel
               approaches, related to the detection of cells in biological
               image processing.  We start by explanation of a PDE-based
               image processing evolution called FBLSCD and study its
               properties. We then define a fully automatic way of finding
               the stop time for this evolution. Afterwards, we try to see
               the FBLSCD as a morphological grayscale erosion, and we
               formulate a novel cell detection algorithm, called LSOpen,
               as an intersection of PDE-based and morphological image
               processing schools.  Then, we discuss the best ways of
               inspecting cell detection results, i.e. cell identifiers. We
               try to quantitatively benchmark various cell detection
               methods by the relative amount of false positives, false
               negatives and multiply-detected centers yielded. We will
               observe that comparing cell detection results in a binary
               fashion is insufficient, therefore we are going to utilize
               the concept of distance function.  Motivated by this need
               for robust cell detection result comparison, we analyze
               commonly-used methods for computing the distance function
               and afterwards we formulate a novel algorithm. This one has
               complexity O(n log2 n) and it yields Euclidean distance. In
               addition to that, we introduce a modification to this
               algorithm, enabling it to work also in maze-like, wall- and
               corner-containing, environments.  This modification relies
               on the line rasterization algorithm. We perform various
               experiments to study and compare distance function methods.
               Results illustrate the viability of newly-proposed method. 
               Further, a software for the comparing and inspecting cell
               detection results, SliceViewer, is specified, designed,
               implemented and tested.  In the end, quantitative
               experiments are discussed, validating the above-mentioned
               novelties. ",
  month =      aug,
  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/2015/Smisek_Michal_A3D/",
}