Kim, Dongkun (2011-08). Automatic Seedpoint Selection and Tracing of Microstructures in the Knife-Edge Scanning Microscope Mouse Brain Data Set. Master's Thesis. Thesis uri icon

abstract

  • The Knife-Edge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. By using the data sets from the KESM, we can trace the neuronal and vascular structures of the whole mouse brain. I investigated effective methods for automatic seedpoint selection on 3D data sets from the KESM. Furthermore, based on the detected seedpoints, I counted the total number of somata and traced the neuronal structures in the KESM data sets. In the first step, the acquired images from KESM were preprocessed as follows: inverting, noise filtering and contrast enhancement, merging, and stacking to create 3D volumes. Second, I used a morphological object detection algorithm to select seedpoints in the complex neuronal structures. Third, I used an interactive 3D seedpoint validation and a multi-scale approach to identify incorrectly detected somata due to the dense overlapping structures. Fourth, I counted the number of somata to investigate regional differences and morphological features of the mouse brain. Finally, I traced the neuronal structures using a local maximum intensity projection method that employs moving windows. The contributions of this work include reducing time required for setting seedpoints, decreasing the number of falsely detected somata, and improving 3D neuronal reconstruction and analysis performance.
  • The Knife-Edge Scanning Microscope (KESM) enables imaging of an entire mouse brain at sub-micrometer resolution. By using the data sets from the KESM, we can trace the neuronal and vascular structures of the whole mouse brain. I investigated effective methods for automatic seedpoint selection on 3D data sets from the KESM. Furthermore, based on the detected seedpoints, I counted the total number of somata and traced the neuronal structures in the KESM data sets.

    In the first step, the acquired images from KESM were preprocessed as follows: inverting, noise filtering and contrast enhancement, merging, and stacking to create
    3D volumes. Second, I used a morphological object detection algorithm to select seedpoints in the complex neuronal structures. Third, I used an interactive 3D seedpoint validation and a multi-scale approach to identify incorrectly detected somata due to the dense overlapping structures. Fourth, I counted the number of somata to investigate regional differences and morphological features of the mouse brain. Finally, I
    traced the neuronal structures using a local maximum intensity projection method that employs moving windows.

    The contributions of this work include reducing time required for setting seedpoints, decreasing the number of falsely detected somata, and improving 3D neuronal reconstruction and analysis performance.

publication date

  • August 2011