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Issue No.08 - August (2009 vol.31)
pp: 1386-1403
Andrew R. Cohen , University of Wisconsin, Milwaukee
Christopher S. Bjornsson , Rensselaer Polytechnic Institute, Troy
Sally Temple , New York Neural Stem Cell Institute, Rensselaer
Gary Banker , Oregon Health and Science University, Portland
Badrinath Roysam , Rensselaer Polytechnic Institute, Troy
An algorithmic information-theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG). Time courses of object states are compared using an adaptive information distance measure, aided by a closed-form multidimensional quantization. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. The summary is the clustering result and feature subset that maximize the gap statistic. This approach was validated on four bioimaging applications: 1) It was applied to a synthetic data set containing two populations of cells differing in the rate of growth, for which it correctly identified the two populations and the single feature out of 23 that separated them; 2) it was applied to 59 movies of three types of neuroprosthetic devices being inserted in the brain tissue at three speeds each, for which it correctly identified insertion speed as the primary factor affecting tissue strain; 3) when applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain; and 4) when analyzing intracellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification.
Image sequence analysis, algorithmic information theory, algorithmic statistics, information distance, gap statistic, clustering.
Andrew R. Cohen, Christopher S. Bjornsson, Sally Temple, Gary Banker, Badrinath Roysam, "Automatic Summarization of Changes in Biological Image Sequences Using Algorithmic Information Theory", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 8, pp. 1386-1403, August 2009, doi:10.1109/TPAMI.2008.162
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