Image processing techniques for assessing contractility in isolated adult cardiac myocytes

International Journal of Biomedical Imaging

Volumn 2009, Issue , 2009, Pages

  Bazan, Carlos ^a   Barba, David Torres ^a   Blomgren, Peter ^b   Paolini, Paul ^b  

^a SAN DIEGO STATE UNIVERSITY   (United States)

^b SAN DIEGO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIAC MYOCYTES; CARDIOCYTES; COMPREHENSIVE ASSESSMENT; COMPUTATIONAL FRAMEWORK; CONTOUR EXTRACTION; CONTRACTILE BEHAVIOR; DIGITAL VIDEO RECORDING; EDGE PRESERVING; FOURIER DESCRIPTORS; IMAGE PROCESSING TECHNIQUE; IMAGE SMOOTHING; IN-CELL; ROBUST ALGORITHM; SEGMENTED IMAGES; SHAPE REPRESENTATION; TOTAL VARIATION;

COMPUTER GRAPHICS; IMAGE PROCESSING; IMAGE RECORDING; IMAGING SYSTEMS; MAMMALS; MULTIMEDIA SYSTEMS; VIDEO RECORDING;

EDGE DETECTION;

EID: 77649227812     PISSN: 16874188     EISSN: 16874196     Source Type: Journal    
DOI: 10.1155/2009/352954     Document Type: Article

References (67)

1. Maltsev V. A., Sabbah H. N., Tanimura M., Lesch M., Goldstein S., Undrovinas A. I., Relationship between action potential, contraction-relaxation pattern, and intracellular Ca2+ transient in cardiomyocytes of dogs with chronic heart failure Cellular and Molecular Life Sciences, vol. 54, no. 6, pp. 597-605, 1998.
2. Ren J., Wold L. E., Measurement of cardiac mechanical function in isolated ventricular myocytes from rats and mice by computerized video-based imaging Biological Procedures Online, vol. 3, no. 1, pp. 43-53, 2001.
3. Banyasz T., Lozinskiy I., Payne C. E., Transformation of adult rat cardiac myocytes in primary culture Experimental Physiology, vol. 93, no. 3, pp. 370-382, 2008.
4. Delbridge L. M. D., Roos K. P., Optical methods to evaluate the contractile function of unloaded isolated cardiac myocytes Journal of Molecular and Cellular Cardiology, vol. 29, no. 1, pp. 11-25, 1997.
5. Mitcheson J. S., Hancox J. C., Levi A. J., Cultured adult cardiac myocytes: future applications, culture methods, morphological and electrophysiological properties Cardiovascular Research, vol. 39, no. 2, pp. 280-300, 1998.
6. Shevchuk A. I., Gorelik J., Harding S. E., Lab M. J., Klenerman D., Korchev Y. E., Simultaneous measurement of Ca2+ and cellular dynamics: combined scanning ion conductance and optical microscopy to study contracting cardiac myocytes Biophysical Journal, vol. 81, no. 3, pp. 1759-1764, 2001.
7. Lecarpentier Y., Martin J.-L., Claes V., Real-time kinetics of sarcomere relaxation by laser diffraction Circulation Research, vol. 56, no. 3, pp. 331-339, 1985.
8. Philips C. M., Duthinh V., Houser S. R., A simple technique to measure the rate and magnitude of shortening of single isolated cardiac myocytes IEEE Transactions on Biomedical Engineering, vol. 33, no. 10, pp. 929-934, 1986.
9. Harris P. J., Stewart D., Cullinan M. C., Delbridge L. M., Dally L., Grinwald P., Rapid measurement of isolated cardiac muscle cell length using a line-scan camera IEEE Transactions on Biomedical Engineering, vol. 34, no. 6, pp. 463-467, 1987.
10. Steadman B. W., Moore K. B., Spitzer K. W., Bridge J. H. B., A video system for measuring motion in contracting heart cells IEEE Transactions on Biomedical Engineering, vol. 35, no. 4, pp. 264-272, 1988.
11. Tameyasu T., Toyoki T., Sugi H., Nonsteady motion in unloaded contractions of single frog cardiac cells Biophysical Journal, vol. 48, no. 3, pp. 461-465, 1985.
12. Mukherjee R., Crawford F. A., Hewett K. W., Spinale F. G., Cell and sarcomere contractile performance from the same cardiocyte using video microscopy Journal of Applied Physiology, vol. 74, no. 4, pp. 2023-2033, 1993.
13. Mukherjee R., Spinale F., Crawford F., Measurement of dynamic cellular and sarcomere contractile properties from the same cardiocyte 2 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society November 1992 392 393
14. Kovai-Milivojevi B., Damsky C. C., Gardner D. G., Ili D., Requirements for the localization of p130 cas to z-lines in cardiac myocytes Cellular and Molecular Biology Letters, vol. 7, no. 2, pp. 323-329, 2002.
15. Klabunde R. E., Cardiovascular Physiology Concepts 2005 Philadelphia, Pa, USA Lippincott Williams Wilkins
16. Katz A., Physiology of the Heart 2006 4th Philadelphia, Pa, USA Lippincott Williams Wilkins
17. Roos K. P., Leung A. F., Theoretical Fraunhofer light diffraction patterns calculated from three-dimensional sarcomere arrays imaged from isolated cardiac cells at rest Biophysical Journal, vol. 52, no. 2, pp. 329-341, 1987.
18. Krueger J. W., Denton A., Siciliano G., Nature of motions between sarcomeres in asynchronously contracting cardiac muscle cells Biophysical Journal, vol. 61, no. 1, pp. 145-160, 1992.
19. Reuben H. E., Godinez M. V., Paolini P. J., Bejar E., Analysis of contractile dynamics of adult cardiac myocytes using a computer-controlled video edge detector: effects of 5-hydroxytryptamine Cardiovascular Pathobiology 1997 2 3-4 149 158
20. Rudin L., Lions P.-L., Osher S., Multiplicative denoising and deblurring: theory and algorithms Geometric Level Set Methods in Imaging, Vision, and Graphics 2003 New York, NY, USA Springer
21. DeVore R. A., Jawerth B., Lucier B. J., Image compression through wavelet transform coding IEEE Transactions on Information Theory 1992 38 2, part 2 719 746
22. Rudin L. I., Osher S., Fatemi E., Nonlinear total variation based noise removal algorithms Physica D, vol. 60, no. 14, pp. 259-268, 1992.
23. Bazn C., PDE-based image and structure enhancement for electron tomography of mitochondria, Ph.D. thesis 2008 San Diego, Calif, USA San Diego State University Claremont Graduate University
24. Bazn C., Blomgren B., Parameter-free adaptive total variation-based noise removal and edge strengthening for mitochondrial structure extraction Research Report 2007 CSRCR 2007-16 San Diego, Calif, USA San Diego State University
25. Strong D. M., Blomgren P., Chan T. F., Spatially adaptive local-feature-driven total variation minimizing image restoration 3167 Statistical and Stochastic Methods in Image Processing II July 1997 San Diego, Calif, USA 222 233 Proceedings of SPIE
26. MathWorks, Detecting a Cell Using Image Segmentation. Image Processing Toolbox 6.3 2008
27. Granlund G., Fourier preprocessing for hand print character recognition IEEE Transactions on Computers, vol. 21, no. 2, pp. 195-201, 1972.
28. Huang C.-L., Huang D.-H., A content-based image retrieval system Image and Vision Computing, vol. 16, no. 3, pp. 149-163, 1998.
29. Kauppinen H., Seppanen T., Pietikainen M., Experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 201-207, 1995.
30. Lu G., Sajjanhar A., Region-based shape representation and similarity measure suitable for content-based image retrieval Multimedia Systems, vol. 7, no. 2, pp. 165-174, 1999.
31. Mehtre B. M., Kankanhalli M. S., Lee W. F., Shape measures for content based image retrieval: a comparison Information Processing and Management, vol. 33, no. 3, pp. 319-337, 1997.
32. Persoon E., Fu K. S., Shape discrimination using Fourier descriptors IEEE Transactions on Systems, Man and Cybernetics, vol. 7, no. 3, pp. 170-179, 1977.
33. Van Otterloo P., Measurement of similarity, mirror-similarity and symmetry A Contour-Oriented Approach to Shape Analysis 1991 chapter 4 London, UK Prentice-Hall
34. Zhang D., Lu G., Content-based shape retrieval using different shape descriptors: a comparative study Proceedings of IEEE International Conference on Multimedia and Expo 2001 Tokyo, Japan 317 320
35. Zahn C., Roskies R., Fourier descriptors for plane closed curves IEEE Transactions on Computers, vol. 21, no. 3, pp. 269-281, 1972.
36. Zhang D., Lu G., A comparative study of Fourier descriptors for shape representation and retrieval Proceedings of the 5th Asian Conference on Computer Vision 2002 Melbourne, Australia 646 651
37. Jain A., Fundamentals of Digital Image Processing 1989 Upper Saddle River, NJ, USA Prentice Hall Information and Systems Science Series
38. Folkers A., Samet H., Content-based image retrieval using fourier descriptors on a logo database 3 Proceedings of the 16th International Conference on Pattern Recognition 2002 Quebec City, Canada 521 524
39. Mingqiang Y., Kidiyo K., Joseph R., A survey of shape feature extraction techniques Pattern Recognition 2008 chapter 3 Vienna, Austria IN-TECH
40. Zhang D., Lu G., A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval Journal of Visual Communication and Image Representation, vol. 14, no. 1, pp. 41-60, 2003.
41. Wier W. G., Kort A. A., Stern M. D., Cellular calcium fluctuations in mammalian heart: direct evidence from noise analysis of aequorin signals in Purkinje fibers Proceedings of the National Academy of Sciences of the United States of America, vol. 80, no. 23, pp. 7367-7371, 1983.
42. Chambolle A., Lions P.-L., Image recovery via total variation minimization and related problems Numerische Mathematik, vol. 76, no. 2, pp. 167-188, 1997.
43. Lions P.-L., Osher S., Rudin L., Denoising and deblurring images using constrained nonlinear partial differential equation 1993 Pasadena, Calif, USA Cognitech
44. Goldfarb D., Yin W., Second-order cone programming methods for total variation-based image restoration SIAM Journal of Scientific Computing, vol. 27, no. 2, pp. 622-645, 2005.
45. Combettes P. L., Pesquet J.-C., Image restoration subject to a total variation constraint IEEE Transactions on Image Processing, vol. 13, no. 9, pp. 1213-1222, 2004.
46. Chambolle A., An algorithm for total variation minimization and applications Journal of Mathematical Imaging and Vision 2004 20 1-2 89 97
47. Darbon J., Sigelle M., Image restoration with discrete constrained total variation part I: fast and exact optimization Journal of Mathematical Imaging and Vision, vol. 26, no. 3, pp. 261-276, 2006.
48. Darbon J., Sigelle M., Image restoration with discrete constrained total variation part II: levelable functions, convex priors and non-convex cases Journal of Mathematical Imaging and Vision, vol. 26, no. 3, pp. 277-291, 2006. (Pubitemid 44916362)
49. Acar R., Vogel C. R., Analysis of bounded variation penalty methods for ill-posed problems Inverse Problems, vol. 10, no. 6, pp. 1217-1229, 1994.
50. Song B., Topics in variational PDE image segmentation, inpainting and denoising, Ph.D. thesis 2003 Los Angeles, Calif, USA University of California Los Angeles
51. Chan T. F., Esedoglu S., Aspects of total variation regularized L 1 function approximation 2004 CAM 05-01 Los Angeles, Calif, USA University of California Los Angeles
52. Strong D., Chan T., Edge-preserving and scale-dependent properties of total variation regularization Inverse Problems 2003 19 6 S165 S187
53. Lysaker M., Tai X.-C., Iterative image restoration combining total variation minimization and a second-order functional International Journal of Computer Vision, vol. 66, no. 1, pp. 5-18, 2006.
54. Marquina A., Osher S., Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal SIAM Journal of Scientific Computing, vol. 22, no. 2, pp. 387-405, 2001.
55. Blomgren P., Chan T. F., Mulet P., Extensions to total variation denoising 3162 Advanced Signal Processing: Algorithms, Architectures, and Implementations VII 1997 367 375 Proceedings of SPIE
56. Chen Y., Levine S., Rao M., Variable exponent, linear growth functionals in image restoration SIAM Journal on Applied Mathematics, vol. 66, no. 4, pp. 1383-1406, 2006.
57. Chan T. F., Golub G. H., Mulet P., Nonlinear primal-dual method for total variation-based image restoration SIAM Journal of Scientific Computing, vol. 20, no. 6, pp. 1964-1977, 1999.
58. Goldstein T., Osher S., The split Bregman algorithm for L1 regularized problems 2008 08-29 Los Angeles, Calif, USA University of California Los Angeles
59. Levine S., Chen Y., Stanich J., Image restoration via nonstandard diffusion 2004 04-01 Pittsburgh, Pa, USA Department of Mathematics and Computer Science, Duquesne University
60. Osher S., Burger M., Goldfarb D., Xu J., Yin W., An iterative regularization method for total variation-based image restoration Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 460-489, 2005.
61. Osher S., Sol A., Vese L., Image decomposition and restoration using total variation minimization and the H-1 norm Multiscale Modeling and Simulation, vol. 1, pp. 349-370, 2003.
62. Bollt E. M., Chartrand R., Esedoglu S., Schultz P., Vixie K. R., Graduated adaptive image denoising: local compromise between total variation and isotropic diffusion Advances in Computational Mathematics, vol. 31, no. 13, pp. 61-85, 2009.
63. Strong D., Chan T., Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing 1996 46 Los Angeles, Calif, USA University of California Los Angeles
64. Vese L. A., Osher S. J., Modeling textures with total variation minimization and oscillating patterns in image processing Journal of Scientific Computing, vol. 19, no. 13, pp. 553-572, 2003.
65. Vogel C. R., Oman M. E., Iterative methods for total variation denoising SIAM Journal of Scientific Computing, vol. 17, no. 1, pp. 227-238, 1996.
66. Gilboa G., Super-resolution algorithms based on inverse diffusion-type processes, Ph.D. thesis 2004 Haifa, Israel Israel Institute of Technology
67. Weickert J., Ter Haar Romeny B. M., Viergever M. A., Efficient and reliable schemes for nonlinear diffusion filtering IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 398-410, 1998.