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Video Bioinformatics Methods for Analyzing Cell Dynamics: A Survey

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Video Bioinformatics

Part of the book series: Computational Biology ((COBO,volume 22))

Abstract

Understanding cellular and subcellular interrelations, spatiotemporal dynamic activities, and complex biological processes from quantitative microscopic video is an emerging field of research. Computational tools from established fields like computer vision, pattern recognition, and machine learning have immensely improved quantification at different stages—from image preprocessing and cell segmentation to cellular feature extraction and selection, classification into different phenotypes, and exploration of hidden content-based patterns in bioimaging databases. This book chapter reviews state of the art in all these stages and directs further research with references from the above-established fields, including key thrust areas like quantitative cell tracking, activity analysis, and cellular video summarization, for enhanced data mining and video bioinformatics.

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References

  1. Aigrain P, Joly O (1994) The automatic real-time analysis of film editing and transition effects and its applications. Comput Graph 18(1):93–103

    Article  Google Scholar 

  2. Akbari H, Uto K, Kosugi Y, Kojima K, Tanaka N (2011) Cancer detection using infrared hyperspectral imaging. Cancer Sci 102(4):852–857

    Article  Google Scholar 

  3. Amanatiadis A, Kaburlasos VG, Gasterator A, Papadakis SE (2011) Evaluation of shape descriptors for shape-based image retrieval. Inst Eng Tech (IET) Image Proc 5(5):493–499

    Google Scholar 

  4. Arslan S, Ersahin T, Cetin-Atalay R, Gunduz-Demir C (2013) Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images. IEEE Trans Med Imaging 32(6):1121–1131

    Article  Google Scholar 

  5. Asada H, Brady M (1986) The curvature primal sketch. IEEE Trans Pattern Anal Mach Intell 8:2–14

    Article  Google Scholar 

  6. Attig A, Perner P (2011). A comparison between Haralick’s texture descriptors and the texture descriptors based on random sets for biological images. In: Perner P (ed) Machine learning and data mining in pattern recognition, vol 6871. Springer. LNAI, pp 524–538

    Google Scholar 

  7. Babaud J, Witkin A, Baudin M, Duda R (1986) Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans Pattern Anal Mach Intell 8:26–33

    Article  MATH  Google Scholar 

  8. Berrada F, Aboutajdine D, Ouatik SE, Lachkar A (2011) Review of 2D shape descriptors based on the curvature scale space approach. Proc Int Conf Multimedia Comput Syst (ICMCS) 1–6

    Google Scholar 

  9. Bhanu B, Pavlidis I (eds) (2005) Computer vision beyond the visible spectrum. Springer

    Google Scholar 

  10. Bhanu B, Chang K, Dong A (2008) Long term cross-session relevance feedback using virtual features. IEEE Trans Knowl Data Eng 20(3):352–368

    Article  Google Scholar 

  11. Bilgin CC, Kim S, Leung E, Chang H, Parvin B (2013) Integrated profiling of three dimensional cell culture models and 3D microscopy. Bioinformatics 29(23):3087–3093

    Article  Google Scholar 

  12. Bishop CM (2006) Pattern recognition and machine learning, 6th edn. Springer, New York

    MATH  Google Scholar 

  13. Boland MV, Murphy RF (2001) A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12):1213–1223

    Article  Google Scholar 

  14. Bose NK, Liang P (1996) Neural networks fundamentals with graphs, algorithms, and applications. McGraw-Hill Publications

    Google Scholar 

  15. Bradski GR, Davis JW (2002) Motion segmentation and pose recognition with motion history gradients. Mach Vis Appl 13:174–184

    Article  Google Scholar 

  16. Brunelli R, Mich O, Modena CM (1999) A survey on the automatic indexing of the video data. J Vis Commun Image Represantation 10:78–112

    Article  Google Scholar 

  17. Buck TE, Li J, Rohde GK, Murphy RF (2012) Toward the virtual cell: Automated approached to building models of subcellular organization “learned” from microscopy images. BioEssays 34(9):791–799

    Article  Google Scholar 

  18. Castaneda V, Cerda M, Santibanez F, Jara J, Pulgar E, Palma K et al (2014) Computational methods for analysis of dynamic events in cell migration. Curr Mol Med 14(2):291–307

    Article  Google Scholar 

  19. Chang HS, Sull S, Lee SU (1999) Efficient video indexing scheme for content-based retrieval. IEEE Trans Circ Syst Video Technol 9(8):1269–1279

    Article  Google Scholar 

  20. Chang H, Fontenay GV, Han J, Cong G, Baehner FL, Gray JW et al (2011) Morphometric analysis of TCGA glioblastoma multiforme. BMC Bioinf 12:484

    Article  Google Scholar 

  21. Chang H, Han J, Spellman PT, Parvin B (2012) Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme. IEEE Trans Biomed Eng 59(12):3460–3467

    Article  Google Scholar 

  22. Chang H, Han J, Borowsky A, Loss L, Gray JW, Spellman PT, Parvin B (2013) Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Trans Med Imaging 32(4):670–682

    Article  Google Scholar 

  23. Chankong T, Theera-Umpon Auephanwiriyakul S (2014) Automatic cervical cell segmentation and classification in Pap smears. Comput Methods Programs Biomed 113(2):539–556

    Article  Google Scholar 

  24. Chen X, Murphy RF (2004) Robust classification of subcellular location patterns in high resolution 3D fluorescence microscope images. Proc Int Conf IEEE Eng Med Biol Soc 1632–1635

    Google Scholar 

  25. Chen S, Zhao M, Wu G, Yao C, Zhang J (2012) Recent advances in morphological cell image analysis. Comput Math Methods Med 10:101536

    MATH  Google Scholar 

  26. Cho BH, Cao-Berg I, Bakal JA, Murphy RF (2012) OMERO.searcher: content-based image search for microscope images. Nat Methods 9(7):633–634

    Article  Google Scholar 

  27. Chung P, Tsai C, Chen F, Sun Y (1994) Polygonal approximation using a competitive Hopfield neural network. Pattern Recogn 27:1505–1512

    Article  Google Scholar 

  28. Coelho LP, Kangas JD, Naik AW, Osuna-Highley E, Glory-Afshar E, Fuhrman M et al (2013) Determining the subcellular location of new proteins from microscope images using local features. Bioinformatics 29(18):2343–2349

    Article  Google Scholar 

  29. Corridoni JM, del Bimbo A (1995) Film semantic analysis. Proc Int Conf Comput Archit Mach Percept (CAMP) 201–209

    Google Scholar 

  30. Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215

    Article  Google Scholar 

  31. Danckaert A, Gonzalez-Couto E, Bollondi L, Thompson N, Hayes B (2002) Automated recognition of intracellular organelles in confocal microscope images. Traffic 3:66–73

    Article  Google Scholar 

  32. Das S, Bhanu B (1998) A system for model-based object recognition in perspective aerial images. Pattern Recogn 31(4):465–491

    Article  Google Scholar 

  33. Davis LS, Mitiche A (1982) Mites: a model-driven, iterative texture segmentation algorithm. Comput Graph Image Process 19:95–110

    Article  Google Scholar 

  34. de Alarcon PA, Gupta A, Carazo JM (1999) A framework for querying a database for structural information on 3D images of macromolecules: a web-based query-by-content prototype on the BioImage macromolecular server. J Struct Biol 125:112–122

    Article  Google Scholar 

  35. Duda RO, Hart PE, Stork DG (2001) Pattern classification (2nd edn). Wiley

    Google Scholar 

  36. Eliceiri KW, Berthold MR, Goldberg IG, Ibanez L, Manjunath BS, Maryann ME et al (2012) Biological imaging software tools. Nat Methods 9(7):697–710

    Article  Google Scholar 

  37. Erdt M, Steger S, Sakas G (2012) Regmentation: a new view of image segmentation and registration. J Radiat Oncol Inf 4(1):1–23

    Google Scholar 

  38. Gargi U, Oswald S, Kosiba DA, Devadiga S, Kasturi R (1995) Evaluation of video sequence indexing and hierarchical video indexing. Proc SPIE Conf Storage Retrieval Image Video Databases III 2420:144

    Article  Google Scholar 

  39. Gerlich D, Mattes J, Elis R (2003) Quantitative motion analysis and visualization of cellular structures. Methods 29:3–13

    Article  Google Scholar 

  40. Ghosh N, Bhanu B (2006) A psychological adaptive model for video analysis. Proc IEEE Int Conf Pattern Recogn (ICPR) 4:346–349

    Google Scholar 

  41. Ghosh N, Bhanu B (2008). How current BNs fail to represent evolvable pattern recognition problems and a proposed solution. Proc. IEEE Intl. Conf. on Pattern Recognition (ICPR). Pg 3618–3621

    Google Scholar 

  42. Ghosh N, Bhanu B, Denina G (2009) Continuous evolvable Bayesian nets for human action analysis in videos. Proc ACM/IEEE Int Conf Distrib Smart Cameras (ICDSC) 194–201

    Google Scholar 

  43. Ghosh N, Bhanu B (2014) Evolving Bayesian graph for 3D vehicle model building from video. IEEE Trans Intell Transp Syst 15(2):563–578

    Google Scholar 

  44. Glocker B, Sotiras A, Komodakis N, Paragios N (2011) Deformable medical image registration: setting the state of the art with discrete methods. Annu Rev Biomed Eng 13:219–244

    Article  Google Scholar 

  45. Grinker S (1980) Edge based segmentation and texture separation. Proc IEEE Int Conf Pattern Recogn 554–557

    Google Scholar 

  46. Guo Y, Zhao G, Peitikainen M (2012) Discriminative features for texture description. Pattern Recogn 45(10):3834–3843

    Article  Google Scholar 

  47. Hanjalic A, Lagendijk RL, Biemond J (1997) A new method for key frame based video content representation. Ser Softw Eng Knowl Eng 8:97–110

    Google Scholar 

  48. Hu Y, Murphy RF (2004) Automated interpretation of subcellular patterns from immunofluorescnece microscopy. J Immunol Methods 290:93–105

    Article  Google Scholar 

  49. Huang K, Murphy RF (2004) From quantitative microscopy to automated image understanding. J Biomed Optics 9(5):893–912

    Article  Google Scholar 

  50. Huang K, Murphy RF (2004b) Automated classification of subcellular patterns in multicell images without segmentation into single cells. Proc IEEE Int Symp Biomed Imaging (ISBI) 1139–1142

    Google Scholar 

  51. IEEE TIP Special Issue (2005) IEEE Trans Image Process. Spec Issue Mol Cell Bioimaging 14(9):1233–1410

    Google Scholar 

  52. Ilea DE, Whelan PF (2011) Image segmentation based on the integration of color-texture descriptors—a review. Pattern Recogn 44(10–11):2479-2501

    Google Scholar 

  53. Imelinska C, Downes MS, Yuan W (2000) Semi-automated color segmentation of anatomical tissue. Comput Med Imaging Graph 24:173–180

    Article  Google Scholar 

  54. Iwano M, Shiba H, Miwa T, Che FS, Takayama S, Nagai T et al (2004) Ca2+ dynamics in a pollen grain and papilla cell during pollination of Arabidopsis. Plant Physiol 136(3):3562–3571

    Article  Google Scholar 

  55. Kafai M, Eshghi K, Bhanu B (2014) Discrete cosine transform based locality-sensitive hashes for retrieval. IEEE Trans Multimedia (In press)

    Google Scholar 

  56. Kamath SB, Chidambar S, Brinda BR, Kumar MA, Sarada R, Ravishankar GA (2005) Digital image processing – an alternative tool for monitoring of pigment levels in cultured cells with special reference to green alga Haematococcus pluvialis. Biosens Bioelectron 21(5):768–773

    Article  Google Scholar 

  57. Korb KB, Nicholson AE (2011) Bayesian artificial intelligence (2nd Edn). Chapman & Hall/CRC Publication

    Google Scholar 

  58. La-Iglesia BD (2013) Evolutionary computation for feature selection in classification problems. Data Min Knowl Disc 3(6):381–407

    Article  Google Scholar 

  59. LaTorre A, Alonos-Nanclares L, Muelas S, Pena JM, DeFelipe J (2013) 3D segmentations of neuronal nuclei from confocal microscope image stacks. Frontiers Neuroanat 7:49

    Article  Google Scholar 

  60. Law AKW, Lam KY, Lam FK, Wong TKW, Poon JLS, Chan HY (2003) Image analysis system for assessment of immunohistochemically stained marker (MIB-1) in oesophageal squamous cell carcinoma. Comput Methods Programs Biomed 70(1):37–45

    Article  Google Scholar 

  61. Lee HC, Kim SD (2003) Iterative key frame selection in the rate-constraint environment. Sig Process Image Commun 18(1):1–15

    Article  Google Scholar 

  62. Li Y, Sorefan K, Hemmann G, Bevan MW (2004) Arabidopsis NAP and PIR regulated Actin-based cell morphogenesis and multiple development processes. Plant Physiol 136(3):3616–3627

    Article  Google Scholar 

  63. Li J, Newberg JY, Uhlen M, Lundberg E, Murphy RF (2012) Automated analysis and reannotation pf subcellular locations in confocal images from the human protein atlas. PLoS ONE 7(11):e50514

    Article  Google Scholar 

  64. Li F, Yin Z, Jin G, Zhao H, Wong STC (2013) Bioimage informatics for systems pharmacology. PLoS Comput Biol 9(4):e1003043

    Article  Google Scholar 

  65. Lin Y, Dou J, Wang H (1992) Contour shape description based on an arch height function. Pattern Recogn 25:17–23

    Article  Google Scholar 

  66. Lin Y, Bhanu B (2005) Object detection via feature synthesis using MDL-based genetic programming. IEEE Trans Syst, Man Cybern, Part B 35(3):538–547

    Article  Google Scholar 

  67. Lindek S, Fritsch R, Machtynger J, de Alarcon PA, Chagoyen M (1999) Design and realization of an on-line database for multidimensional microscopic images of biological specimens. J Struct Biol 125(2):103–111

    Article  Google Scholar 

  68. Liu H, Srinath M (1990) Partial shape classification using contour matching in distance transformation. IEEE Trans Pattern Anal Mach Intell 12(11):1072–1079

    Article  Google Scholar 

  69. Liu Y, Dellaert F (1998) A classification based similarity metric for 3D image retrieval. Proc IEEE Int Conf Comput Vis Pattern Recogn (CVPR) 800–805

    Google Scholar 

  70. Loncaric S (1998) A survey of shape analysis techniques. Pattern Recogn 31(8):983–1001

    Article  Google Scholar 

  71. Loss L, Bebis G, Parvin B (2011) Iterative tensor voting for perceptual grouping of ill-defined curvilinear structures. IEEE Trans Med Imaging 30(8):1503–1513

    Article  Google Scholar 

  72. Lumia R, Haralick RM, Zuniga O, Shapiro L, Pong TC, Wang FP (1983) Texture analysis of aerial photographs. Pattern Recogn 16(1):39–46

    Article  Google Scholar 

  73. Maragos P (1989) Pattern spectrum and multiscale shape representation. IEEE Trans Pattern Anal Mach Intell 11(7):701–716

    Article  MATH  Google Scholar 

  74. Marx V (2002) Beautiful bioimaging for the eyes of many beholders. Science 297:39–40

    Article  Google Scholar 

  75. Meijering E, Niessen W, Weickert J, Viergever M (2002) Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: results of an in-vitro evaluation. Med Image Anal 6(3):215–233

    Article  MATH  Google Scholar 

  76. Meijering E (2010) Neuron tracing in perspective. Cytometry Part A 77A(7):693–704

    Article  MathSciNet  Google Scholar 

  77. Miles A, Zhao J, Klyne G, White-Cooper H, Shotton D (2010) OpenFlyData: An exemplar data web integrating gene expression data on the fruit fly Drosophila melanogaster. J Biomed Inf 43(5):752–761

    Article  Google Scholar 

  78. Mirabella R, Franken C, van der Krogt GNM, Bisseling T, Geurts R (2004) Use of the fluorescent timer DsRED-E5 as reporter to monitor dynamics of gene activity in plants. Plant Physiol 135(4):1879–1887

    Article  Google Scholar 

  79. Mitchell T (1997) Machine learning. McGraw Hill Publication

    Google Scholar 

  80. Mohamadlou H, Shope JC, Flann NS (2014) Maximizing kolmogorov complexity for accurate and robust breight field cell segmentation. BMC Bioinf 15(1):32

    Article  Google Scholar 

  81. Molina LC, Belanche L, Nebot A (2002) Feature selection algorithms: a survey and experimental evaluation. Proc IEEE Int Conf Data Min (ICDM) 306–313

    Google Scholar 

  82. Murphy RF, Velliste M, Porreca G (2003) Robust numerical features for description and classification of the subcellular location patterns in fluorescence microscope images. J VLSI Sign Process Syst Sign, Image Video Technol 35(3):311–321

    Article  MATH  Google Scholar 

  83. Murphy RF (2011) An active role for machine learning in drug development. Nat Chem Biol 7(6):327–330

    Article  Google Scholar 

  84. Murphy RF (2012) Cell Organizer: Image-derived models of subcellular organization and protein distribution. Methods Cell Biol 110:179–193

    Article  Google Scholar 

  85. Nagasaka A, Tanaka Y (1991) Automatic video indexing and full-video search for object appearances. Proc Working Conf Vis Database Syst 119–133

    Google Scholar 

  86. Nam D, Mantell J, Bull D, Verkade P, Achim A (2014) A novel framework for segmentation of secretory granules in electron microscopy. Med Image Anal 2014(18):411–424

    Article  Google Scholar 

  87. Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125

    Article  Google Scholar 

  88. Naik AW, Kangsas JD, Langmead CJ, Murphy RF (2013) Efficient modeling and active learning discovery of biological responses. PLoS ONE 8(12):e83996

    Article  Google Scholar 

  89. Nayak N, Chang H, Borowsky A, Spellman P, Parvin B (2013) Classification of tumor histopathology via sparse feature learning. Proc IEEE Int Symp Biomed Imaging 410–413

    Google Scholar 

  90. Newberg J, Hua J, Murphy RF (2009) Location proteomics: systematic determination of protein subcellular location. Methods Mol Biol 500:313–332

    Article  Google Scholar 

  91. Nosrati MS, Hamarneh G (2013) Segmentation of cells with partial occlusion and part configuration constraint using evolutionary computation. Med Image Comput Assist Interv 16(Pt 1):461–468

    Google Scholar 

  92. Okada T, Miller MJ, Parker I, Krummel MF, Neighbors M, Sb Hartley et al (2005) Antigen-engaged B cells undergo chemotaxis toward the T zone and form motile conjugates with helper T cells. PLoS Biol 3(6):e150

    Article  Google Scholar 

  93. Oliveira FP, Tavares JM (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93

    Article  Google Scholar 

  94. Parvin B, Fontenay G, Yang Q, Barcellos-Hoff MH (2003) BioSig: an imaging bioinformatics system for phenotypic analysis. IEEE Trans Syst, Man, Cybernatics Part B 33(5):814–824

    Google Scholar 

  95. Parvin B, Ghosh N, Heiser L, Knapp M, Talcott C, Laderoute K, et al (2007) Spectral decomposition of signaling networks. Proc IEEE Symp Comput Intell Bioinf Comput Biol (CIBCB) 76–81

    Google Scholar 

  96. Peng H (2008) Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17):1827–1836

    Article  Google Scholar 

  97. Peng T, Murphy RF (2011) Image-derived, three-dimensional generative models of cellular organization. Cytometry Part A 79A(5):383–391

    Article  Google Scholar 

  98. Peng H, Roysam B, Ascoli GA (2013) Automated image computing reshapes computational neuroscience. BMC Bioinf 14:293

    Article  Google Scholar 

  99. Pietimainen M, Rosenfeld A (1981) Image segmentation by texture using pyramid node linking. IEEE Trans Syst Man Cybern 11(12):822–825

    Article  Google Scholar 

  100. Raafat HM, Wong AKC (1980) Texture information directed algorithm for biological image segmentation and classification. Proc Int Conf Cybern Soc 1003–1008

    Google Scholar 

  101. Raafat HM, Wong AKC (1986) Texture based image segmentation. Proc IEEE Int Conf Comput Vis Pattern Recogn (CVPR) 469–475

    Google Scholar 

  102. Reed TR, Wechsler H, Werman M (1990) Texture segmentation using a diffusion region growing technique. Pattern Recogn 23(9):953–960

    Article  Google Scholar 

  103. Reed TR, Dubuf JM-H (1993) A review of recent texture segmentation and feature extraction techniques. Comput Vis Graph Image Process: Image Underst 57(3):359–372

    Article  Google Scholar 

  104. Ritter G, Schreib G (2001) Using dominant points and variants for profile extraction from chromosomes. Pattern Recogn 34(4):923–938

    Article  MATH  Google Scholar 

  105. Rittscher J (2010) Characterization of biological processes through automated image analysis. Annu Rev Biomed Eng 12:315–344

    Article  Google Scholar 

  106. Rizk A, Paul G, Incardona P, Buqarski M, Mansouri M, Neimann A et al (2014) Segmentation and quantification of subcellular structures in fluorescence microscopy imaged using Squassh. Nat Protoc 9(3):586–596

    Article  Google Scholar 

  107. Rodriguez A, Shotton DM, Trelles O, Guil N (2000) Automatic feature extraction in wound healing videos. In: Proceedings of 6th RIAO conference on content-based multimedia information access. Paris

    Google Scholar 

  108. Ronneberger O, Baddeley D, Scheipi F, Verveer PJ, Burkhardt H, Cremer C et al (2008) Spatial quantitative analysis of fluorescently labeled nuclear structures: problems, methods, pitfalls. Chromosom Res 16(3):523–562

    Article  Google Scholar 

  109. Sabri S, Richelme F, Pierres A, Benoliel AM, Bongrand P (1997) Interest of image processing in cell biology and immunology. J Immunol Methods 208(1):1–27

    Article  Google Scholar 

  110. Salton G, Buckley C (1990) Improving retrieval performance by relevance feedback. J Am Soc Inf Sci 41(4):288–297

    Article  Google Scholar 

  111. Schoenenmann T, Cremers D (2006) Near real-time motion segmentation using graph cuts. In: Franke K et al (Eds) Pattern recognition. Springer, LNCS 4174, Berlin, pp 455–464

    Google Scholar 

  112. Sethi IK, Patel N (1995) A statistical approach to scene change detection. Proc SPIE Symp Storage Retrieval Image Video Databases III 2420:329–338

    Article  Google Scholar 

  113. Shahrary B, Gibbon DC (1995) Automatic generation of pictorial transcript of video programs. Proc SPIE Digital Video Compression: Algorithms Technol 2420:512–518

    Google Scholar 

  114. Shapiro LG, Stockman GC (2001) Computer vision. Prentice Hall Publications

    Google Scholar 

  115. Shariff A, Kangas J, Coelho LP, Quinn S, Murphy RF (2010) Automated image analysis for high-content screening and analysis. J Biomol Screen 15(7):726–734

    Article  Google Scholar 

  116. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  117. Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 11(9):739–747

    Article  Google Scholar 

  118. Shotton DM, Rodriguez A, Guil N, Trelles O (2000) Object tracking and event recognition in biological microscopy videos. Proc IEEE Int Conf Pattern Recogn (ICPR) 4:226–229

    Article  Google Scholar 

  119. Soll DR, Wessels D (eds) (1998) Motion Analysis of Living Cells. Wiley-Liss Publication, New York

    Google Scholar 

  120. Sommer C, Gerlich DW (2013) Machine learning in cell biology – teaching computers to recognize phenotypes. J Cell Sci 126(24):5529–5539

    Article  Google Scholar 

  121. Spann M, Wilson R (1985) A quad-tree approach to image segmentation which combines statistical and spatial information. Pattern Recogn 18(3):257–269

    Article  Google Scholar 

  122. Tachino RM, Kabuyama N, Gotoh T, Kagei S, Naruse M, Kisu Y et al (2003) High-throughput classification of images of cells transfected with cDNA clones. CR Biol 326(10):993–1001

    Article  Google Scholar 

  123. Thomann D, Dorn J, Sorger PK, Danuser G (2003) Automatic fluorescence tag localization II: improvement in super-resolution by relative tracking. J Microsc 211(3):230–248

    Article  MathSciNet  Google Scholar 

  124. Tuceryan M, Jain AK (1998) Texture analysis. In: Chen CH, Pau LF, Wang PSP (eds) The handbook of pattern recognition and computer vision (2nd edn). World Scientific Publishing Co Pte Ltd 207–248

    Google Scholar 

  125. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  126. Verbeek PW, DeJong DJ (1984) Edge preserving texture analysis. Proc IEEE Int Conf Pattern Recogn (ICPR) 1030–1032

    Google Scholar 

  127. Volkmann N (2010) Methods for segmentation and interpretation of electron tomographic reconstructions. Methods Enzymol 483:31–46

    Article  Google Scholar 

  128. Wang G, Song Q, Sun H, Zhang X, Xu B, Zhou Y (2003) A feature subset selection algorithm automatic recommendation method. J Artif Intell Res (JAIR) 47:1–34

    MATH  Google Scholar 

  129. Weiss Y (1999) Segmentation using eigenvectors: a unifying view. Proc IEEE Int Conf Comput Vis (ICCV) 2:975–982

    Google Scholar 

  130. Wermser D (1984) Unsupervised segmentation by use of a texture gradient. Proc IEEE Int Conf Pattern Recogn (ICPR) 1114–1116

    Google Scholar 

  131. Willis B, Turner JN, Collins DN, Roysam B, Holmes TJ (1993) Developments in three-dimensional stereo brightfield microscopy. Microsc Res Tech 24(5):437–451

    Article  Google Scholar 

  132. Witt CM, Roychaudhuri S, Schaefer B, Chakraborty AK, Robey EA (2005) Directed migration of positively selected thymocytes visualized in real time. PLoS Biol 3(6):e160

    Article  Google Scholar 

  133. Wolf W (1996) Key frame selection by motion analysis. Proc IEEE Int Conf Acoust, Speech, Sign Process (ICASSP) 2:1228–1231

    Google Scholar 

  134. Wolf L, Shashua A (2005) Feature selection for unsupervised and supervised interface: the emergence of sparsity in a weight-based approach. J Mach Learn Res 6:1855–1887

    MathSciNet  MATH  Google Scholar 

  135. Xiao J, Shah M (2005) Motion layer extraction in the presence of occlusion using graph cut. IEEE Trans Pattern Anal Mach Intell 27(10):1644–1659

    Article  Google Scholar 

  136. Xiong W, Li CM, Ma RH (1997) Automatic video data structuring through shot partitioning and key-frame computing. Mach Vis Appl 10(2):51–65

    Article  Google Scholar 

  137. Yang G (2013) Bioimage informatics for understanding spatiotemporal dynamics of cellular processes. Wiley Interdisc Rev: Syst Biol Med 5(3):367–380

    Google Scholar 

  138. Yang Q, Parvin B (2003) Harmonic cut and regularized centroid transform for localization of subcellular structures. IEEE Trans Biomed Eng 50(4):469–475

    Article  Google Scholar 

  139. Yeung MM, Liu B (1995) Efficient matching and clustering of video shots. Proc IEEE Int Conf Image Process (ICIP) 1:338–342

    Article  Google Scholar 

  140. Yilmaz A, Javed O, Shah M (2006) Object tracking: A survey. ACM Comput Surv 38(4):13

    Article  Google Scholar 

  141. Zabih R, Miller J, Mai K (1999) A feature-based algorithm for detecting and classifying production effects. Multimedia Syst 7(2):119–128

    Article  Google Scholar 

  142. Zappella L, Llado X, Salvi J (2008) Motion segmentation: a review. Proc Conf Artif Intell Res Dev 398–407

    Google Scholar 

  143. Zaritsky A, Natan S, Horev J, Hecht I, Wolf L, Ben-Jacob E, Tsarfaty I (2011) Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images. PLoS ONE 6(11):e27593

    Article  Google Scholar 

  144. Zhang HJ, Kankanhalli A, Smoliar SW (1993) Automatic partitioning of full-motion video. Multimedia Syst 1(1):10–28

    Article  Google Scholar 

  145. Zhang J, Tan T (2002) Brief review of invariant texture analysis methods. Pattern Recogn 35(3):735–747

    Article  MATH  Google Scholar 

  146. Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19

    Article  MATH  Google Scholar 

  147. Zhao J, Miles A, Klyne G, Shotton D (2009) Linked data and provenance in biological data webs. Briefings Bioinf 10(2):139–152

    Article  Google Scholar 

  148. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to acknowledge Department of Pediatrics at Loma Linda University (LLU) School of Medicine, Loma Linda, CA, USA and Center for Research in Intelligent Systems at University of California Riverside (UCR) Department of Electrical Engineering, Riverside, CA, USA for supporting his research over the years—especially Dr. Stephen Ashwal (LLU) and Dr. Bir Bhanu (UCR) for encouraging exploratory research.

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  1. Department of Pediatrics, School of Medicine, Loma Linda University, Loma Linda, CA, USA

    Nirmalya Ghosh

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  1. Nirmalya Ghosh
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Correspondence to Nirmalya Ghosh .

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  1. University of California, Riverside, California, USA

    Bir Bhanu

  2. University of California, Riverside, California, USA

    Prue Talbot

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Ghosh, N. (2015). Video Bioinformatics Methods for Analyzing Cell Dynamics: A Survey. In: Bhanu, B., Talbot, P. (eds) Video Bioinformatics. Computational Biology, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-23724-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-23724-4_2

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