||The primary end-goal of most medical imaging research program is to collect information 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, backgroundinhomogeneity, and other artifacts leading to fuzzy representation 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. During
my talk, first, I will present a new framework for fuzzy skeletonization and its applications. Finally, I
will briefly mention about other research works from my laboratory and other collaborative research
groups which are related to fuzzy digital topology and geometry.
Skeletonization provides a simple yet compact representation of an object and is widely used in medical
imaging applications including volumetric, structural, and topological analyses, object representation,
stenoses detection, path-finding etc. Literature of three-dimensional skeletonization is quite matured
for binary objects. However, the challenges of skeletonization for fuzzy objects are mostly unanswered.
Here, a framework and an algorithm for fuzzy surface skeletonization are developed using a notion of
fuzzy grassfire propagation. Several concepts including fuzzy axial voxels, local and global significance
factors are introduced. A skeletal noise pruning algorithm using global significance factors of individual
branches is developed.