The Community for Technology Leaders
RSS Icon
Issue No.08 - August (2009 vol.31)
pp: 1386-1403
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.
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
[1] C.H. Bennett, P. Gacs, L. Ming, M.B. Vitanyi, and W.H. Zurek, “Information Distance,” IEEE Trans. Information Theory, vol. 44, pp. 1407-1423, 1998.
[2] R. Cilibrasi and P.M.B. Vitanyi, “Clustering by Compression,” IEEE Trans. Information Theory, vol. 51, pp. 1523-1545, 2005.
[3] M. Li, X. Chen, X. Li, B. Ma, and P.M.B. Vitanyi, “The Similarity Metric,” IEEE Trans. Information Theory, vol. 50, pp. 3250-3264, 2004.
[4] P. Gacs, J. Tromp, and P. Vitanyi, “Algorithmic Statistics,” IEEE Trans. Information Theory, vol. 47, pp. 2443-2463, 2001.
[5] N.K. Vereshchagin and P.M.B. Vitanyi, “Kolmogorov's Structure Functions and Model Selection,” IEEE Trans. Information Theory, vol. 50, pp. 3265-3290, 2004.
[6] P. Vitanyi, “Meaningful Information,” IEEE Trans. Information Theory, vol. 52, pp. 4617-4626, 2006.
[7] R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image Change Detection Algorithms: A Systematic Survey,” IEEE Trans. Image Processing, vol. 14, p. 294, 2005.
[8] K.A. Al-Kofahi, S. Lasek, D.H. Szarowski, C.J. Pace, G. Nagy, J.N. Turner, and B. Roysam, “Rapid Automated Three-Dimensional Tracing of Neurons from Confocal Image Stacks,” IEEE Trans. Information Technology in Biomedicine, vol. 6, p. 171, 2002.
[9] O. Al-Kofahi, R.J. Radke, S.K. Goderie, Q. Shen, S. Temple, and B. Roysam, “Automated Cell Lineage Tracing: A High-Throughput Method to Analyze Cell Proliferative Behavior Developed Using Mouse Neural Stem Cells,” Cell Cycle, vol. 5, pp. 327-335, Feb. 2006.
[10] O. Al-Kofahi, R.J. Radke, B. Roysam, and G. Banker, “Automated Semantic Analysis of Changes in Image Sequences of Neurons in Culture,” IEEE Trans. Biomedical Eng., vol. 53, pp. 1109-1123, 2006.
[11] O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Tracking of Migrating Cells under Phase-Contrast Video Microscopy with Combined Mean-Shift Processes,” IEEE Trans. Medical Imaging, vol. 24, pp. 697-711, 2005.
[12] H. Narasimha-Iyer, A. Can, B. Roysam, H.L. Tanenbaum, and A. Majerovics, “Integrated Analysis of Vascular and Non-Vascular Changes from Color Retinal Fundus Image Sequences,” IEEE Trans. Biomedical Eng., vol. 54, pp. 1436-1445, Aug. 2007.
[13] N. Roussel, C.A. Morton, F.P. Finger, and B. Roysam, “A Computational Model for C. elegans Locomotory Behavior: Application to Multi-Worm Tracking,” IEEE Trans. Biomedical Eng., vol. 54, pp. 1786-1797, Oct. 2007.
[14] J.A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, R.K. Jain, and B. Roysam, “Robust Modeling of 2-D/3-D Microvasculature Imagery Using Super-Gaussians,” IEEE Trans. Medical Imaging, vol. 26, pp. 223-237, Feb. 2007.
[15] T.M. Cover and J.A. Thomas, Elements of Information Theory. John Wiley & Sons, 1991.
[16] M. Li and P.M.B. Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications, second ed. Springer, 1997.
[17] E. Keogh, S. Lonardi, and C.A. Ratanamahatana, “Towards Parameter-Free Data Mining,” Proc. ACM SIGKDD, 2004.
[18] M. Cebrian, M. Alfonseca, and A. Ortega, “The Normalized Compression Distance Is Resistant to Noise,” IEEE Trans. Information Theory, vol. 53, pp. 1895-1900, May 2007.
[19] P. Grünwald, I.J. Myung, and M. Pitt, Advances in Minimum Description Length: Theory and Applications. MIT Press, 2005.
[20] J. Rissanen, Stochastic Complexity in Statistical Inquiry. World Scientific, 1989.
[21] Z. Bao, J.I. Murray, T. Boyle, S.L. Ooi, M.J. Sandel, and R.H. Waterston, “Automated Cell Lineage Tracing in Caenorhabditis elegans,” Proc. Nat'l Academy Sciences USA, vol. 103, pp. 2707-2712, 2006.
[22] C. Ronse, L. Najman, and E. Decenciere, “Mathematical Morphology: 40 Years On,” Computational Imaging and Vision 30. Springer, 2005.
[23] G. Loy and A. Zelinsky, “Fast Radial Symmetry for Detecting Points of Interest,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 959-993, Aug. 2003.
[24] D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int'l J. Computer Vision, vol. 60, pp. 91-110, 2004.
[25] D. Reid, “An Algorithm for Tracking Multiple Targets,” IEEE Trans. Automatic Control, vol. 24, p. 843, 1979.
[26] J. Munkres, “Algorithms for the Assignment and Transportation Problems,” J. SIAM, vol. 5, pp. 32-38, Mar. 1957.
[27] A. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm,” IEEE Trans. Information Theory, vol. 13, pp. 260-269, Apr. 1967.
[28] F. Pitie, S.A. Berrani, A. Kokaram, and R. Dahyot, “Off-Line Multiple Object Tracking Using Candidate Selection and the Viterbi Algorithm,” Proc. IEEE Int'l Conf. Image Processing, vol. 3, pp. 109-112, 2005.
[29] P.P. Pradeep and P.F. Whelan, “Tracking of Facial Features Using Deformable Triangles,” Proc. SPIE, vol. 4877, S. Andrew, D.M.Fionn, M. James, and F.W. Paul, eds., pp. 138-143, 2003.
[30] C. Stauffer and W.E.L. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug. 2000.
[31] B. Katz, J. Lin, C. Stauffer, and E. Grimson, “Answering Questions about Moving Objects in Surveillance Videos,” Proc. AAAI Spring Symp. New Directions in Question Answering, Mar. 2003.
[32] P. Heas and M. Datcu, “Modeling Trajectory of Dynamic Clusters in Image Time-Series for Spatio-Temporal Reasoning,” IEEE Trans. Geoscience and Remote Sensing, vol. 43, pp. 1635-1647, 2005.
[33] G. Medioni, I. Cohen, F. Bremond, S. Hongeng, and R. Nevatia, “Event Detection and Analysis from Video Streams,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 873-889, Aug. 2001.
[34] E. Gokcay, E. Gokcay, and J.C. Principe, “Information Theoretic Clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 158-171, Feb. 2002.
[35] G.J. Chaitin, Information, Randomness and Incompleteness: Papers on Algorithmic Information Theory. World Scientific, 1990.
[36] C.E. Au, S. Skaff, and J.J. Clark, “Anomaly Detection for Video Surveillance Applications,” Proc. 18th Int'l Conf. Pattern Recognition, pp. 888-891, 2006.
[37] B. Anton, F. Miquel, B. Imma, and M. Sbert, “Compression-Based Image Registration,” Proc. IEEE Int'l Symp. Information Theory, pp.436-440, 2006.
[38] R. Tibshirani, G. Walther, and T. Hastie, “Estimating the Number of Clusters in a Dataset via the Gap Statistic,” J. Royal Statistical Soc., vol. 63, pp. 411-423, 2001.
[39] Y.G. Leclerc, “Constructing Simple Stable Descriptions for Image Partitioning,” Int'l J. Computer Vision, vol. 3, pp. 73-102, May 1989.
[40] P. Adriaans and P.M.B. Vitanyi, “The Power and Perils of MDL,” Proc. IEEE Int'l Symp. Information Theory, 2007.
[41] M. Yi, D. Harm, and H. Wei, “Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 9, pp. 1547-1562, Sept. 2007.
[42] J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,” Proc. Eighth ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery June 2003.
[43] V. Megalooikonomou, Q. Wang, G. Li, and C. Faloutsos, “A Multiresolution Symbolic Representation of Time Series,” Proc. 21st Int'l Conf. Data Eng., pp. 668-679, 2005.
[44] R.M. Gray and D.L. Neuhoff, “Quantization,” IEEE Trans. Information Theory, vol. 44, pp. 2325-2383, 1998.
[45] D. Wood, Theory of Computation. Harper & Row, 1987.
[46] P.N. Yianilos, “Normalized Forms for Two Common Metrics,” technical report, NEC Research Inst., Princeton, N.J., Dec. 1991.
[47] A.Y. Ng, M. Jordan, and Y. Weiss, “On Spectral Clustering: Analysis and an Algorithm,” Advances in Neural Information Processing Systems, vol. 14, 2002.
[48] G. Hamerly and C. Elkan, “Learning the k in kmeans,” Advances in Neural Information Processing Systems, vol. 17, 2003.
[49] L. Chen and M.T. Ozsu, “Multi-Scale Histograms for Answering Queries over Time Series Data,” Proc. 20th Int'l Conf. Data Eng., p.838, 2004.
[50] J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,” Data Mining and Knowledge Discovery J., vol. 15, pp.107-144, Oct. 2007.
[51] V. Chew, “Confidence, Prediction, and Tolerance Regions for the Multivariate Normal Distribution,” J. Am. Statistical Assoc., vol. 61, pp. 605-617, Sept. 1966.
[52] Vector Quantization. IEEE Press, 1990.
[53] L. Huan and Y. Lei, “Toward Integrating Feature Selection Algorithms for Classification and Clustering,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 4, pp. 491-502, Apr. 2005.
[54] P. Pudil, F.J. Ferri, J. Novovicova, and J. Kittler, “Floating Search Methods for Feature Selection with Nonmonotonic Criterion Functions,” Pattern Recognition, vol. 2, pp. 279-283, 1994.
[55] G. Lin, M.K. Chawla, K. Olson, C.A. Barnes, J.F. Guzowski, and B. Roysam, “A Multi-Model Approach to Simultaneous Segmentation and Classification of Heterogeneous Populations of Cell Nuclei in 3D Confocal Microscope Images,” Cytometry, vol. 71A, 2007.
[56] L.J. Bain and M. Engelhardt, Introduction to Probability and Mathematical Statistics. Duxbury, 1992.
[57] J.G. Mitchell and K. Kogure, “Bacterial Motility: Links to the Environment and a Driving Force for Microbial Physics,” FEMS Microbiology Ecology, vol. 55, pp. 3-16, 2006.
[58] C.A. Ratanamahatana and E. Keogh, “Three Myths about Dynamic Time Warping,” Proc. SIAM Int'l Conf. Data Mining, 2005.
[59] M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh, “Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures,” Proc. ACM SIGKDD '03, pp. 216-225, 2003.
[60] D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 142-149, 2000.
[61] S.K. Zhou and R. Chellappa, “From Sample Similarity to Ensemble Similarity: Probabilistic Distance Measures in Reproducing Kernel Hilbert Space,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 6, p. 917-929, June 2006.
[62] C. Bjornsson, S.J. Oh, Y.A. Al-Kofahi, Y.J. Lim, K.L. Smith, J.N. Turner, S. De, B. Roysam, W. Shain, and S.J. Kim, “Shape- and Insertion Rate Dependent Tissue Damage Due to Neuroprosthetic Device Insertion,” J. Neural Eng., vol. 3, pp. 196-207, 2006.
[63] Q. Gao, M. Li, and P.M.B. Vitanyi, “Applying MDL to Learning Best Model Granularity,” Artificial Intelligence, vol. 121, pp. 1-29, 2000.
[64] G. Jacobson, B. Schnapp, and G.A. Banker, “A Change in the Selective Translocation of the Kinesin-1 Motor Domain Marks the Initial Specification of the Axon,” Neuron, vol. 49, pp. 797-804, 2006.
[65] A.R. Cohen, C. Bjornsson, S. Temple, G. Banker, and B. Roysam, “Automatic Summarization of Changes in Image Sequences using Algorithmic Information Theory,” Proc. Fifth IEEE Int'l Symp. Biomedical Imaging, 2008.
42 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool