Dynamic time warping and cross correlation for the estimation of cell movements in conjunctival capillaries: A comparison through simulation

IFMBE Proceedings

Volumn 25, Issue 11, 2009, Pages 112-115

  Grisan, E ^a   Tiso, A ^a   Ruggeri, A ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Cell velocity; Cross correlation; Dynamic time warping

Indexed keywords

BIOMEDICAL ENGINEERING; BIOPHYSICS; BLOOD; BLOOD VESSELS; CYTOLOGY; DYNAMICS; ERRORS; MEDICAL PHYSICS; MICROCIRCULATION; OPHTHALMOLOGY; VELOCITY;

AUTOMATIC ANALYSIS; BLOOD CELLS VELOCITY; CROSS CORRELATION METHODS; CROSS CORRELATIONS; DYNAMIC TIME WARPING; LONGITUDINAL VARIATIONS; ONE DIMENSIONAL SIGNAL; VELOCITY VARIATIONS;

CELLS;

EID: 77950156218     PISSN: 16800737     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-03891-4_30     Document Type: Conference Paper

References (9)

1. M. Rudzsky, H. Bitterman, V. Brod, E. Rivlin, E. Eden, and D. Waisman. An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy. IEEE Transactions Medical Imaging, 10:1011, 2005.
2. C.G. Ellis, M.L. Ellsworth, R.N. Pittman, and W.L. Burgess. Application of image analysis for evaluation of red blood cell dynamics in capillaries. Microvascular Research, 44:214-225, 1992.
3. S. Japee, C.G. Ellis, and R.N. Pittman. Flow visualiztion tool for image analysis of capillary networks. Microcirculation, 11:39-54, 2004.
4. F. Itakura. Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech, and Signal, ASSP-23:52-72, 1975.
5. J.B. Kruskall and M. Liberman. Time Warps, String Edits and Macromolecules: The Theory and Practice of String Comparison, chapter The symmetric time warping algorithm: From continuous to discrete.
6. E. Keogh and M. Pazzani. Scaling up dynamic time warping for datamining applications. In 6th ACM International Conference on Knowledge Discovery and Data Mining, 2000.
7. E. Grisan, , A. Tiso, and A. Ruggeri. Estimation of real-time red blood cell velocity in conjunctival vessels using a modified dynamic-time-warping approach. In IFMBE Proceedings of the Fourth European Congress for Medical & Biological Engineering Conference, EMBEC'08, November 2008.
8. E.J. Keogh and M.J. Pazzani. Derivative dynamic time warping. In Proceedings of the 1st SIAM International Conference on Data Mining, 2001.
9. D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In AAAI-94 Workshop on Knowledge Discovery in Databases, 1994.