Automatic summarization of changes in biological image sequences using algorithmic information theory

Andrew R. Cohen, Christopher S. Bjornsson, Sally Temple, Gary Banker, Badrinath Roysam

Research output: Contribution to journalArticle

35 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1386-1403
Number of pages18
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume31
Issue number8
DOIs
StatePublished - Jun 9 2009

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Keywords

  • Algorithmic information theory
  • Algorithmic statistics
  • Clustering distance
  • Image sequence analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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