@inproceedings{abeysinghe2008grayskeletonPaper,
 author = {Sasakthi S. Abeysinghe and Tao Ju},
 title = {Segmentation-free Skeletonization of Grayscale Volumes for Shape Understanding},
 booktitle = {IEEE International Conference on Shape Modeling and Applications},
 year = {2008},
 pages = {63--71},
 location = {Stony Brook, New York},
 isbn = {978-1-4244-2260-9},
 publisher = {IEEE},
 note = {accepted},
 abstract = {Medical imaging has produced a large number of volumetric
    images capturing biological structures in 3D. Computer-based
    understanding of these structures can often benefit from the
    knowledge of shape components, particularly rod-like and
    plate-like parts, in such volumes. Previously, skeletons have
    been a common tool for identifying these shape components in
    a solid object. However, obtaining skeletons of a grayscale
    volume poses new challenges due to the lack of a clear
    boundary between object and background. In this paper, we
    present a new skeletonization algorithm on grayscale volumes
    typical to medical imaging (e.g., MRI, CT and EM scans), for
    the purpose of identifying shape components. Our algorithm
    does not require an explicit segmentation of the volume into
    object and background, and is capable of producing skeletal
    curves and surfaces that lie centered at rod-shaped and
    plate-shaped parts in the grayscale volume. Our method is
    demonstrated on both synthetic and medical data. },
 }

@inproceedings{abeysinghe2007grayskeletonPoster,
 author = {Sasakthi S. Abeysinghe and Tao Ju},
 title = {Shape-preserving gray-scale skeletonization on 3D density maps},
 booktitle = {SIGGRAPH '07: ACM SIGGRAPH 2007 posters},
 year = {2007},
 pages = {20},
 location = {San Diego, California},
 doi = {http://doi.acm.org/10.1145/1280720.1280743},
 publisher = {ACM},
 address = {New York, NY, USA},
 }