Artificial Intelligence Review

, Volume 51, Issue 4, pp 577–646 | Cite as

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

  • Chen Li
  • Kai Wang
  • Ning Xu


Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semi- or full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future development trend in this interdisciplinary field. In addition, the frequently-used CBMIA methods are further analysed to find technological common points and potential reasons.


Microorganism classification Content-based microscopic image analysis Feature extraction Classifier design 


  1. Aguzzi J, Costa C, Costa C, Robert K, Matabos M, Antonucci F, Juniper SK, Menesatti P (2011) Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network. Sensors 11(11):10,534–10,556Google Scholar
  2. Akiba T, Kahui Y (2000) Design and testing of an underwater microscope and image processing system for the study of zooplankton distribution. IEEE J Ocean Eng 25(1):97–104Google Scholar
  3. Albertano P (2000) Image analysis for qualitative and quantitative evaluation of planktic cyanobacteria. In: Workshop. Freshwater harmful algal blooms: health risk and control management. Istituto Superiore di Sanità. Rome, 17 October 2000. Proceedings edited by Serena elchiorre, Emanuela Viaggiu and Milena Bruno 2002, 103 p. Rapporti ISTISAN 02/9 (in Italian and English)Google Scholar
  4. Almeida VED, Costa GBD, Fernandes DDDS, Diniz PHGD, Brandao D, Medeiros ACDD, Veras G (2014) Using color histograms and SPA-LDA to classify bacteria. Anal Bioanal Chem 406(24):5989–5995Google Scholar
  5. Alvarez E, Lopez-Urrutia A, Gurira E (2012) Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM. J Plankton Res 34(6):454–469Google Scholar
  6. Alvarez-Borrego J, Mourino-Perez RR, Cristobal G, Pech-Pacheco JL (2000) Invariant optical colour correlation for recognition of vibrio cholerae 01. In: International conference on pattern recognition, pp 283–286Google Scholar
  7. Amaral AL (2003) Image analysis in biotechnological processes: applications to wastewater treatment. PhD Dissertation in the University of MinhoGoogle Scholar
  8. Amaral AL, Baptiste C, Pons MN, Nicolau A, Lima N, Ferreira EC, Mota M, Vivier H (1999) Semi-automated recognition of protozoa by image analysis. Biotechnol Tech 13(2):111–118Google Scholar
  9. Amaral AL, Motta MD, Pons MN, Vivier H, Roche N, Mota M, Ferreira EC (2004) Survey of protozoa and metazoa populations in wastewater treatment plants by image analysis and discriminant analysis. Environmentrics 15(4):381–390Google Scholar
  10. Amaral AL, Ginoris YP, Nicolau A, Coelho MAZ, Ferreira EC (2008) Stalked protozoa identification by image analysis and multivariable statistical techniques. Anal Bioanal Chem 319(4):1321–1325Google Scholar
  11. Anikster Y, Eilam T, Bushnell WR, Kosman E (2005) Spore dimensions of Puccinia species of cereal hosts as determined by image analysis. Mycologia 97(2):474–484Google Scholar
  12. Ayas S, Ekinci M (2014) Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples. SIViP 8(1):49–61Google Scholar
  13. Bachiller E, Fernandes JA (2011) Zooplankton image analysis manual: automated identification by means of scanner and digital camera as imaging devices. Rev Invest Mar 18(2):17–37Google Scholar
  14. Baek J, Cosman P, Feng Z, Silver J, Schafer WR (2002) Using machine vision to analyze and classify caenorhabditis elegans behavioral phenotypes quantitatively. J Neurosci Methods 118(1):9–21Google Scholar
  15. Balafar MA, Ramli AR, Mashohor S (2010a) A new method for MR grayscale inhomogeneity correction. Artif Intell Rev 34(2):195–204Google Scholar
  16. Balafar MA, Ramli AR, Sarlpan MI, Mashohor S (2010b) Review of brain MRI image segmentation methods. Artif Intell Rev 33(3):261–274Google Scholar
  17. Balfoort HW, Snoek J, Smits JRM, Breedveld LW, Hofstraat JW, Ringelberg J (1992) Automatic identification of algae: neural network analysis of flow cytometric data. J Plankton Res 14(4):575–589Google Scholar
  18. Beaufort L, Dollfus D (2004) Automatic recognition of coccoliths by dynamical neural networks. Mar Micropaleontol 51(1–2):57–73Google Scholar
  19. Bell JL, Hopgroft RR (2008) Assessment of ZooImage as a tool for the classification of zooplankton. J Plankton Res 30(12):1351–1367Google Scholar
  20. Bernhard D, Leipe DD, Sogin ML, Schlegel KM (1995) Phylogenetic relationships of the Nassulida within the phylum Ciliophora inferred from the complete small subunit RRNA gene sequences of Furgasonia blochmanni, Obertrumia georgiana, and Pseudomicrothorax dubius. J Eukaryot Microbiol 42(2):126–131Google Scholar
  21. Blackburn N, Hagstrom A, Wikner J, Cuadros-Hansson R, Bjornsen PK (1998) Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis. Appl Environ Microbiol 64(9):3246–3255Google Scholar
  22. Boelter M, Moeller R, Dzomla W (1993) Determination of bacterial biovolume with epifluorescence microscopy: comparison of size distributions from image analysis and size classifications. Micron 24(1):31–40Google Scholar
  23. Boucher A, Doisy A, Ronot X, Garbay C (1998) Cell migration analysis after in vitro wounding injury with a multi-agent approach. Artif Intell Rev 12(1–3):137–162Google Scholar
  24. Brenner M (2006) Engineering microorganisms for energy production. Report in the MITRE Corporation JASON Program OfficeGoogle Scholar
  25. Castro-Longoria E, Alvarez-Borrego J, Pech-Pacheco JL (2001) Identification of species of calanoid copepods using a new invariant correlation algorithm. Crustaceana 74(10):1029–1039Google Scholar
  26. Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 3(2):1–27Google Scholar
  27. Chang C, Ho P, Sastri AR, Lee Y, Gong G, Hsieh C (2012a) Methods of training set construction: towards improving performance for automated mesozooplankton image classification systems. Cont Shelf Res 36:19–28Google Scholar
  28. Chang J, Arbelaez P, Switz N, Reber C, Tapley A, Davis JL, Cattamanchi A, Fletcher D, Malik1 J (2012b) Automated tuberculosis diagnosis using fluorescence images from a mobile microscope. Med Image Comput Assist Interv 15(Pt 3):345–352Google Scholar
  29. Chen C, Li X (2008) A new wastewater bacteria classification with microscopic image analysis. In: WSEAS international conference on computers, pp 915–921Google Scholar
  30. Chen S, Feng X, Li Y, Zhou C, Xi P, Ren Q (2010) Software controlling algorithms for the system performance optimization of confocal laser scanning microscope. Biomed Signal Process Control 5(3):223–228Google Scholar
  31. Chin LK, Ayi TC, Yap PH, Liu AQ (2011) Protozoon classifications based on size, shape and refractive index using on-chip immersion refractometer. In: International solid-state sensors, actuators and microsystems conference, pp 771–774Google Scholar
  32. Chwojnowski A, Przytulska M, Wierzbicka D, Kulikowski J, Wojciechowski C (2012) Membranes porosity evaluation by computer-aided analysis of sem images a preliminary study. Biocybern Biomed Eng 32(4):65–75Google Scholar
  33. Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Passarelli V, Gualtieri P (2013) Automatic and real time recognition of microalgae by means of pigment signature and shape. Environ Sci Process Impacts 15:1397–1410Google Scholar
  34. Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Gualtieri P (2014) Water monitoring: automated and real time identification and classification of algae using digital microscopy. Environ Sci Process Impacts 16(11):2656–2665Google Scholar
  35. Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Gualtieri P (2016) Reconstruction of the absorption spectrum of an object spot from the colour values of the corresponding pixel(s) in its digital image: the challenge of algal colours. J Microsc 264(3):311–320Google Scholar
  36. Costa JC, Mesquita DP, Amaral AL, Alves MM, Ferreira EC (2013) Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review. Environ Sci Pollut Res 20(9):5887–5912Google Scholar
  37. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27zbMATHGoogle Scholar
  38. Cox PW, Thomas CR (1992) Classification and measurement of fungal pellets by automated image analysis. Biotechnol Bioeng 39(9):945–952Google Scholar
  39. Culverhouse P, Herry V, Parisini T, Williams R, Reguera B, Gonzalez-Gil S, Fonda S, Cabrini M (2000) DiCANN: a machine vision solution to biological specimen categorisation. In: Proceedings of the EurOCEAN 2000 Conference, pp 239–240Google Scholar
  40. Culverhouse PF, Ellis R, Simpson RG, Williams R, Pierce RW, Turner JT (1994) Automatic categorisation of five species of Cymatocylis (Protozoa, Tintinnida) by artificial neural network. Mar Ecol Prog Ser 107:273–280Google Scholar
  41. Culverhouse PF, Simpson RG, Ellis R, Lindley JA, Williams R, Parsini T, Reguera B, Bravo I, Zoppoli R, Earnshaw G, McCall H, Smith GC (1996) Automatic classification of field-collected dinoflagellates by artificial neural network. Mar Ecol Prog Ser 139(1–3):281–287Google Scholar
  42. Culverhouse PF, Williams R, Reguera B, Herryl V, Gonzalez-Gil S (2003) Expert and machine discrimination of marine flora: a comparison of recognition accuracy of field-collected phytoplankton. In: International Conference on Visual Information Engineering, pp 177–181Google Scholar
  43. Culverhouse PF, Williams R, Benfield M, Flood PR, Sell AF, Mazzocchi MG, Buttino I, Sieracki M (2006a) Automatic image analysis of plankton: future perspectives. Mar Ecol Prog Ser 312:297–309Google Scholar
  44. Culverhouse PF, Williams R, Simpson B, Gallienne C, Reguera B, Cabrini M, Fonda-Umani S, Parisini T, Pellegrino FA, Pazos Y, Wang H, Escalera L, Morono A, Hensey M, Silke J, Pellegrini A, Thomas D, James D, Longa MA, Kennedy S, Punta GD (2006b) HAB Buoy: a new instrument for in situ monitoring and early warning of harmful algal bloom events. Afr J Mar Sci 28(2):245–250Google Scholar
  45. Daims H, Luecker S, Wagner M (2006) Daime, a novel image analysis program for microbial mcology and biofilm research. Environ Microbiol 8(2):200–213Google Scholar
  46. Das M, Butterworth F, Das R (1996) Statistical signal modeling techniques for automated recognition of water-borne microbial shapes. In: IEEE 39th midwest symposium on circuits and systems, pp 613–616Google Scholar
  47. Dazzo FB (2010) CMEIAS digital microscopy and quantitative image analysis of microorganisms. Microsc Sci Technol Appl Educ 2(4):1083–1090Google Scholar
  48. Dazzo FB, Gross C (2013a) CMEIAS quadrat maker: a digital software tool to optimize grid dimensions and produce quadrat images for landscape ecology spatial analysis. Ecosyst Ecogr 3(4):1–4Google Scholar
  49. Dazzo FB, Gross C (2013b) In situ ecophysiology of microbial biofilm communities analyzed by CMEIAS computer-assisted microscopy at single-cell resolution. Diversity 5(3):426–460Google Scholar
  50. Dazzo FB, Niccum BC (2015) Use of CMEIAS image analysis software to accurately compute attributes of cell size, morphology, spatial aggregation and color segmentation that signify in situ ecophysiological adaptations in microbial biofilm communities. Computation 3(1):72–98Google Scholar
  51. Dieleman S (2015) Classifying plankton with deep neural networks.
  52. Dietrich A, Uhlig G (1984) Stage specific classification of copepods with automatic image analysis. In: Proceedings of the First International Conference on Copepoda, pp 159–165Google Scholar
  53. Ding K, Gunasekaran S (1998) Three dimensional image reconstruction procedure for food microstructure evaluation. Artif Intell Rev 12(1–3):245–262Google Scholar
  54. Dorado AP (2016) Automatic recognition of diatoms and its applications to the study of water quality. PhD Dissertation in the Universidad de Castilla-La ManchaGoogle Scholar
  55. Dubuisson M, Jain AK, Jain MK (1994) Segmentation and classification of bacterial culture images. J Microbiol Methods 19(4):279–295Google Scholar
  56. Durant G, Cox PW, Formisyn P, Thomas CR (1994a) Improved Image analysis algorithm for the characterisation of mycelial aggregates after staining. Biotechnol Tech 8(11):759–764Google Scholar
  57. Durant G, Grawley G, Formisyn P (1994b) A simple straining procedure for the characterisation of basidiomycetes pellets by image analysis. Biotechnol Tech 8(6):395–400Google Scholar
  58. Elbischger PJ, Bischof H, Regitnig P, Holzapfei GA (2004) Automatic analysis of collagen fiber orientation in the outermost layer of human arteries. Pattern Anal Appl 7(3):269–284MathSciNetGoogle Scholar
  59. Embleton KV, Gibson CE, Heaney SI (2003) Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method. J Plankton Res 25(6):669–681Google Scholar
  60. Estep KW, MacIntyre F (1989) Counting, sizing, and identification of algae using image analysis. Sarsia 74(4):261–268Google Scholar
  61. Fernandez-Canque H, Hintea S, Csipkes G, Pellow A, Smith H (2008) Machine vision application to the detection of micro-organism in drinking water. In: Goebel R, Siekmann J, Wahlster W (eds) Knowledge-based intelligent information and engineering systems. Springer, New York, pp 336–343Google Scholar
  62. Ferreira T, Rasband W (2012) Image user guide.
  63. Fields S, Johnston M (2005) Cell biology. Whither model organism research? Science 307(5717):1885–1886Google Scholar
  64. Filho CFFC, Levy PC, Xavier CDM, Fujimoto LBM, Costa MGF (2015) Automatic identifi cation of tuberculosis mycobacterium. Res Biomed Eng 31(1):33–43Google Scholar
  65. Forero MG, Cristbal G, Alvarez J (2003) Automatic identification techniques of tuberculosis bacteria. In: Proceeding of SPIE 5203, applications of digital image processing XXVI, pp 71–81Google Scholar
  66. Forero MG, Cristobal G, Desco M (2006) Automatic identification of mycobacterium tuberculosis by gaussian mixture models. J Microsc 223(2):120–132MathSciNetGoogle Scholar
  67. Forero-Vargas MG, Sroubek F, Alvarez-Borrego J, Malpica N, Cristobal G, Santos A, Alcala L, Desco M, Cohen L (2002) Segmentation, autofocusing, and signature extraction of tuberculosis sputum images. In: Processing of SPIE 4788, photonic devices and algorithms for computing IV, pp 1–12Google Scholar
  68. Fukuda T, Hasegawa O (1989) Expert system driven image processing for recognition and identification of micro-organisms. In: International workshop on industrial applications of machine intelligence and vision, pp 33–38Google Scholar
  69. Garcia-Comas, Picheral (2013) Short manual to scan and process samples using the ZOOSCAN.
  70. Geng W (2004) A machine vision and statistical learning system for studying C. elegans phenotypes. PhD Dissertation in University of California, San DiegoGoogle Scholar
  71. Geng W, Cosman P, Baek J, Berry CC, Schafer WR (2003) Quantitative classification and natural clustering of caenorhabditis elegans behavioral phenotypes. Genetics 165(3):1117–1126Google Scholar
  72. Geng W, Cosman P, Berry CC, Feng Z, Schafer WR (2004) Automatic tracking, feature extraction and classification of C. elegans phenotypes. IEEE Trans Biomed Eng 51(10):1811–1820Google Scholar
  73. Gerlach SR, Siedenberg D, Gerlach D, Schtigerl K, Giuseppin MLF, Hunik J (1998) Influence of reactor systems on the morphology of Aspergillus awamori. application of neural network and cluster analysis for characterization of fungal morphology. Process Biochem 33(6):601–615Google Scholar
  74. Gillespie SH, Bamford KB (2012) Medical microbiology and infection at a glance, 4th edn. Wiley-Blackwell, New YorkGoogle Scholar
  75. Ginoris YP, Amaral AL, Nicolau A, Ferreira EC, Coelho MAZ (2006) Recognition of protozoa and metazoa using image analysis tools, discriminant analysis and neural network. In: International conference on chemometrics in analytical chemistry, p 1Google Scholar
  76. Ginoris YP, Amaral AL, Nicolau A, Coelho MAZ, Ferreira EC (2007a) Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system. Water Res 41(12):2581–2589Google Scholar
  77. Ginoris YP, Amaral AL, Nicolau A, Ferreira EC, Coelho MAZ (2007b) Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees. Anal Chim Acta 595(1–2):160–169Google Scholar
  78. Gonzalez P, Alvarez E, Barranquero J, Diez J, Gonzalez-Quiros R, Nogueira E, Lopez-Urrutia A, Coz JJD (2013) Multiclass support vector machines with example-dependent costs applied to plankton biomass estimation. IEEE Trans Neural Netw Learn Syst 24(11):1901–1905Google Scholar
  79. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Pearson International Edition, New YorkGoogle Scholar
  80. Gorsky G, Ohman MD, Picheral M, Gasparini S, Stemmann L, Romagnan J, Cawood A, Pesant S, Garcia-Comas C, Prejger F (2010) Digital zooplankton image analysis using the ZOOSCAN integrated system. J Plankton Res 32(3):285–303Google Scholar
  81. Gray AJ, Young D, Martin NJ, Glasbey CA (2002) Cell identification and sizing using digital image analysis for estimation of cell biomass in high rate algal ponds. J Appl Phycol 14(3):193–204Google Scholar
  82. Greenwood SJ, Sogin ML, Lynn DH (1991) Phylogenetic relationships within the class Oligohymenophorea, Phylum Ciliophora, inferred from the complete small subunit rRNA gene sequences of Colpidium campylum, Glaucoma chattoni, and Opisthonecta henneguyi. J Mol Evol 33(2):163–174Google Scholar
  83. Griffiths EC (2010) What is a model? Archived March 12, 2012, at the Wayback MachineGoogle Scholar
  84. Grosjean P, Picheral M, Warembourg C, Gorsky G (2004) Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system. ICES J Mar Sci 61(4):518–525Google Scholar
  85. Grzegorzek M (2010) A system for 3D texture-based probabilistic object recognition and its applications. Pattern Anal Appl 13(3):333–348MathSciNetGoogle Scholar
  86. Grzegorzek M, Li C, Raskatow J, Paulus D, Vassilieva N (2013) Texture-based text detection in digital images with wavelet features and support vector machines. In: Burduk R, Jackowski K, Kurzynski M, Wozniak M, Zolnierek A (eds) Advances in intelligent systems and computing. Springer, New York, pp 857–866Google Scholar
  87. Guo G, Dyer CR (2005) Learning from examples in the small sample case: face expression recognition. Syst Man Cybern Part B Cybern 35(3):477–488Google Scholar
  88. Gutzeit E, Scheel C, Dolereit T, Rust M (2014) Contour based split and merge segmentation and pre-classification of zooplankton in very large images. In: International conference on computer vision theory and applications, pp 417–424Google Scholar
  89. Hand DJ, Yu K (2001) Idiot’s bayes-not so stupid after All? Int Stat Rev 69(3):385–398zbMATHGoogle Scholar
  90. Hiremath PS, Bannigidad P (2009) Automatic classification of bacterial cells in digital microscopic images. Int J Eng Technol 2(4):9–15Google Scholar
  91. Hiremath PS, Bannigidad P (2010a) Automatic identification and classification of bacilli bacterial cell growth phases. Int J Comput Appl Spec Issue RTIPPR 1:48–52Google Scholar
  92. Hiremath PS, Bannigidad P (2010b) Digital image analysis of cocci bacterial cells using active contour method. In: International conference on signal and image processing, pp 163–168Google Scholar
  93. Hiremath PS, Bannigidad P (2011a) Digital microscopic image analysis of spiral bacterial cell groups. In: International conference on intelligent systems & data processing, pp 209–213Google Scholar
  94. Hiremath PS, Bannigidad P (2011b) Identification and classification of cocci bacterial cells in digital microscopic images. Int J Comput Biol Drug Des 4(3):262–273Google Scholar
  95. Hiremath PS, Bannigidad P (2012) Spiral bacterial cell image analysis using active contour method. Int J Comput Appl 37(8):5–9Google Scholar
  96. Hoshi K, Shingai R (2006) Computer-driven automatic identification of locomotion states in Caenorhabditis elegans. J Neurosci Methods 157(2):355–363Google Scholar
  97. Hu Q (2006) Application of statistical learning theory to plankton image analysis. PhD Dissertation in the Massachusetts Institute of Technology and the Woods Hole Oceanographic InstitutionGoogle Scholar
  98. Hu Q, Davis C (2005) Automatic plankton image recognition with co-occurrence matrices and support vector machine. Mar Ecol Prog Ser 295:21–31Google Scholar
  99. Hu Q, Davis C (2006) Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction. Mar Ecol Prog Ser 306:51–61Google Scholar
  100. Huang K, Cosman P, Schafer WR (2006) Machine vision based detection of omega bends and reversals in C. elegans. J Neurosci Methods 158(2):323–336Google Scholar
  101. Huang K, Cosman P, Schafer WR (2007) Automated tracking of multiple C. elegans with articulated models. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 1240–1243Google Scholar
  102. Huang X, Li C, Shen M, Shirahama K, Nyffeler J, Leist M, Grzegorzek M, Deussen O (2016) Stem cell microscopic image segmentation using supervised normalised cuts. In: IEEE international conference on image processing, pp 4140–4144Google Scholar
  103. Ishii T, Adachi R, Omori M, Shimizu U, Irie H (1987) The identification, counting, and measurement of phytoplankton by an image-processing system. ICES J Mar Sci 43(3):253–260Google Scholar
  104. Jain AK, Hong L (1996) Automatic classification of bacteria culture images. Report in Michigan State UniversityGoogle Scholar
  105. Jalba AC, Roerdink MHFWJBTM, Bayer MM, Juggins S (2005) Automatic diatom identification using contour analysis by morphological curvature scale spaces. Mach Vis Appl 16(4):217–228Google Scholar
  106. Javidi B, Moon I, Yeom S, Carapezza E (2005) Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography. Opt Express 13(12):4492–4506Google Scholar
  107. Javidi B, Moon I, Daneshpanah M (2006a) 3D Imaging, visualization, and recognition of biological microorganisms. In: Proceedings of SPIE6392, three-dimensional TV, video, and display V, pp 639,202–1–639,202–10Google Scholar
  108. Javidi B, Moon I, Yeom S (2006b) 3D Imaging and visualization of biological microorganisms. In: Annual meeting of the IEEE lasers and electro-optics society, pp 709–710Google Scholar
  109. Javidi B, Moon I, Yeom S (2006c) Real-time automated 3D visualizing and recognition of biological microorganisms. In: Proceedings of SPIE 6311, optical information systems IV, pp 631,103–1–631,103–8Google Scholar
  110. Javidi B, Moon I, Yeom S (2006d) Three-dimensional identification of biological microorganism using integral imaging. Opt Express 14(25):12,096–12,108Google Scholar
  111. Javidi B, Moon I, Yeom S, Carapezza E (2006e) 3D imaging and recognition of microorganism using single-exposure online (SEOL) digital holography. In: Javidi B, Carapezza E, Huignard J, Nasrabadi N, Tiziani H, Tschudi T, Watson EA, Yatagai T (eds) Advanced sciences and technologies for security applications. Springer, New York, pp 139–156Google Scholar
  112. Javidi B, Yeom S, Moon I, Carapezza E (2006f) Three-dimensional imaging and recognition of microorganisms using computational holography. In: Proceedings of SPIE 6234, automatic target recognition XVI, pp 623,405–1–623,405–8Google Scholar
  113. Javidi B, Yeom S, Moon I, Daneshpanah M (2006g) Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events. Opt Express 14(9):3806–3829Google Scholar
  114. Javidi B, Daneshpanah M, Moon I (2010a) Three-dimensional holographic imaging for identification of biological micro/nanoorganisms. IEEE Photon J 2(2):256-259Google Scholar
  115. Javidi B, Moon I, Daneshpanah M (2010b) Detection, identification and tracking of biological micro/nano organisms by computational 3D optical imaging. In: Proceedings of SPIE 7759, Biosensing III, pp 77,590R–1–77,590R–6Google Scholar
  116. Jay JM, Loessner MJ, Golden DA (2005) Modern food microbiology, 7th edn. Springer, New YorkGoogle Scholar
  117. Jeffries HP, Berman MS, Poularikas AD, Katsinis C, Melas I, Sherman K, Bivins L (1984) Automated sizing, counting and identification of zooplankton by pattern recognition. Mar Biol 78(3):329–334Google Scholar
  118. Jenne R, Cenens C, Impe JFV (2001) Towards on-line quantification of flocs and filaments by means of image analysis for optimization and control of activated sludge plants. Biotechnol Lett 66(3a):63–70Google Scholar
  119. Jenne R, Cenens C, Geeraerd AH, Impe JFV (2002) Towards on-line quantification of flocs and filaments by image analysis. Biotechnol Lett 24(11):931–935Google Scholar
  120. Jenne R, Banadda EN, Philips N, Impe JFV (2003) Image analysis as a monitoring tool for activated sludge properties in lab-scale installations. J Environ Sci Health A 38(10):2009–2018Google Scholar
  121. Ji Z, Card KJ, Dazzo FB (2015) CMEIAS JFrad: a digital computing tool to discriminate the fractal geometry of landscape architectures and spatial patterns of individual cells in microbial biofilms. Microb Ecol 69(3):710–720Google Scholar
  122. Jiang T (2016) Stereo vision for facet type cameras. Logos Verlag Berlin GmbH, GermanyGoogle Scholar
  123. John J, Nair MS, Kumar PRA, Wilscy M (2016) A novel approach for detection and delineation of cell nuclei using feature similarity index measure. Biocybern Biomed Eng 36(1):76–88Google Scholar
  124. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  125. Jothi JAA, Rajam VMA (2017) A survey on automated cancer diagnosis from histopathology images. Artif Intell Rev 48:31–81Google Scholar
  126. Kato K (1996) Image analysis of bacterial cell size and diversity. In: Colwell RR, Simidu U, Ohwada K (eds) Microbial diversity in time and space. Plenum Press, New York, pp 141–147Google Scholar
  127. Kay JW, Shinn AP, Sommerville C (1999) Towards an automated system for the identification of notifiable pathogens: using gyrodactylus salaris as an example. Parasitol Today 15(5):201–206Google Scholar
  128. Khutlang R, Krishnan S, Whitelaw A, Douglas TS (2009) Detection of tuberculosis in sputum smear images using two one-class classifiers. In: IEEE international symposium on biomedical imaging, pp 1007–1010Google Scholar
  129. Khutlang R, Krishnan S, Dendere R, Whitelaw A, Veropoulos K, Learmonth G, Douglas TS (2010a) Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears. IEEE Trans Inf Technol Biomed 14(4):949–957Google Scholar
  130. Khutlang R, Krishnan S, Whitelaw A, Douglas TS (2010b) Automated detection of tuberculosis in Ziehl–Neelsen-stained sputum smears using two one-class classifiers. J Microsc 237(1):96–102MathSciNetGoogle Scholar
  131. Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M, Arje J, Karkkainen S, Tirronen V, Juhola M, Turpeinen T, Meissner K (2011) Classification and retrieval on macroinvertebrate image databases. Comput Biol Med 41(7):463–472Google Scholar
  132. Kirkpatrick GJ, Millie DF, Moline MA, Schofield O (2000) Optical discrimination of a phytoplankton species in natural mixed populations. Assoc Sci Limnol Oceanogr 45(2):467–471Google Scholar
  133. Kishida K (2005) Property of average precision and its generalization: an examination of evaluation indicator for information retrieval experiments. NII technical report, NII-2005-014E, in National Institute of InformaticsGoogle Scholar
  134. Kocak DM, Lobo NDV, Widder EA (1999) Computer vision techniques for quantifying, tracking, and identifying bioluminescent plankton. IEEE J Ocean Eng 24(1):81–95Google Scholar
  135. Koren Y, Sznitman R, Arratia PE, Carls C, Krajacic P, Brown AEX, Sznitman J (2015) Model-independent phenotyping of C. elegans locomotion using scale-invariant feature transform. PLoS ONE 10(3):1–16Google Scholar
  136. Korzynska A, Strojny W, Hoppe A, Wertheim D, Hoser P (2007) Segmentation of microscope images of living cells. Pattern Anal Appl 10(4):301–319MathSciNetGoogle Scholar
  137. Kramer KA (2005) identifying plankton from grayscale silhouette images. Master Thesis in University of South FloridaGoogle Scholar
  138. Kruk M, Kozera R, Osowski S, Trzcinski P, Paszt LS, Sumorok B, Borkowski B (2015) Computerized classification system for the identification of soil microorganisms. In: AIP conference proceedings 1648(660018):1–4Google Scholar
  139. Kruk M, Kozera R, Osowski S, Trzcinski P, Sas-Paszt L, Sumorok B, Borkowski B (2016) Computerized classification systemfor the identification of soil microorganisms. Appl Math Inf Sci 10(1):21–31Google Scholar
  140. Kumar S, Mittal GS (2008) Geometric and optical characteristics of five microorganisms for rapid detection using image processing. Biosyst Eng 99(1):1–8Google Scholar
  141. Kumar S, Mittal GS (2009) Textural characteristics of five microorganisms for rapid detection using image processing. J Food Process Eng 32(1):126–143Google Scholar
  142. Kumar S, Mittal GS (2010) Rapid detection of microorganisms using image processing parameters and neural network. Food Bioprocess Technol 3(5):741–751Google Scholar
  143. Lakshmi S, Sankaranarayanan V (2010) A study of edge detection techniques for segmentation computing approaches. Int J Comput Appl Spec Issue Comput Aided Soft Comput Tech Imaging Biomed Appl 1:35–41Google Scholar
  144. Langford RE (2004) Introduction to weapons of mass destruction: radiological, chemical, and biological. Wiley-IEEE, New YorkGoogle Scholar
  145. Leal-Taixe L, Heydt M, Weisse S, Rosenhahn A, Rosenhahn B (2010) Classification of swimming microorganisms motion patterns in 4D digital in-line holography data. In: Goesele M, Roth S, Kuijper A, Schiele B, Schindler K (eds) Pattern Recognition. Springer, New York, pp 283–292Google Scholar
  146. Lecault V, Patel N, Thibault J (2007) Morphological characterization and viability assessment of trichoderma reesei by image analysis. Biotechnol Prog 22(3):734–740Google Scholar
  147. Lee MS, Lim JS, Kim CH, Oh KK, Yang DR, Kim SW (2001) Enhancement of cephalosporin C production by cultivation of cephalosporium acremonium M25 using a mixture of inocula. Lett Appl Microbiol 32(6):402–406Google Scholar
  148. Li C (2016) Content-based microscopic image analysis. Logos Verlag Berlin GmbH, BerlinGoogle Scholar
  149. Li C, Shirahama K, Czajkowska J, Grzegorzek M, Ma F, Zhou B (2013a) A multi-stage approach for automatic classification of environmental microorganisms. In: International conference on image processing, computer vision, and pattern recognition, pp 364–370Google Scholar
  150. Li C, Shirahama K, Grzegorzek M, Ma F, Zhou B (2013b) Classification of environmental microorganisms in microscopic images using shape features and support vector machines. In: IEEE international conference on image processing, pp 2435–2439Google Scholar
  151. Li C, Shirahama K, Grzegorzek M (2015a) Application of content-based image analysis to environmental microorganism classification. Biocybern Biomed Eng 35(1):10–21Google Scholar
  152. Li C, Shirahama K, Grzegorzek M (2015b) Environmental microbiology aided by content-based image analysis. Pattern Anal Appl 19(2):531–547MathSciNetGoogle Scholar
  153. Li C, Shirahama K, Grzegorzek M (2015c) Environmental microorganism classification using sparse coding and weakly supervised learning. In: International workshop on environmental multimedia retrieval in conjunction with ACM international conference on multimedia retrieval, pp 9–14Google Scholar
  154. Li C, Huang X, Jiang T, Xu N (2017) Full-automatic computer aided system for stem cell clustering using content based microscopic image analysis. Biocybern Biomed Eng (Online First)Google Scholar
  155. Li X, Chen C (2007) A novel bacteria recognition method based on microscopic image analysis. N Z J Agric Res 50(5):697–703Google Scholar
  156. Li X, Chen C (2008) A novel wastewater bacteria recognition method based on microscopic image analysis. In: WSEAS international conference on circuits, systems, electronics, control and signal processing, pp 265–271Google Scholar
  157. Li X, Chen C (2009) An improved BP neural network for wastewater bacteria recognition based on microscopic image analysis. WSEAS Trans Comput 8(2):237–247Google Scholar
  158. Li X, Chen C, Liang A, Shi Y (2007a) Local and global features extracting and fusion for microbial recognition. In: ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, pp 507–511Google Scholar
  159. Li X, Chen C, Yv Z (2007b) A novel bacteria classification scheme based on microscopic image analysis. In: WSEAS international conference on applied computer science, pp 447–451Google Scholar
  160. Li Z, Zhao F, Liu J, Qiao Y (2014) Pairwise nonparametric discriminant analysis for binary plankton image recognition. IEEE J Ocean Eng 39(4):695–701Google Scholar
  161. Liu J, Dazzo FB, Glagoleva O, Yu B, Jain AK (2001) CMEIAS: a computer-aided system for the image analysis of bacterial morphotypes in microbial communities. Microb Ecol 41(3):173–194Google Scholar
  162. Lomander A, Schreuders P, RussekCohen E, Ali L (2002) A method for rapid analysis of biofilm morphology and coverage on glass and polished and brushed stainless steel. Trans ASAE 45(2):479–487Google Scholar
  163. Luo T (2005) Scaling up support vector machines with application to plankton recognition. PhD Dissertation in University of South FloridaGoogle Scholar
  164. Luo T, Kramer K, Goldgof D, Hall LO, Samson S, Remsen A, Hopkins T (2003) Learning to recognize plankton. In: IEEE international conference on systems, man and cybernetics, pp 888–893Google Scholar
  165. Luo T, Kramer K, Goldgof DB, Hall LO, Samson S, Remsen A, Hopkins T (2004) Recognizing plankton images from the shadow image particle profiling evaluation recorder. IEEE Trans Syst Man Cybern Part B Sybern 34(4):1753–1762Google Scholar
  166. Mackey MD, Mackey DJ, Higgins HW, Wright SW (1996) CHEMTAX-a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser 144:265–283Google Scholar
  167. Madigan M, Martinko J (2006) Brock Biology of Microorganisms, 13th edn. Pearson Education, Upper Saddle RiverGoogle Scholar
  168. Makkapati V, Agrawal R, Acharya R (2009) Segmentation and classification of tuberculosis bacilli from ZN-stained Sputum Smear Images. In: IEEE international conference on automation science and engineering, pp 217–220Google Scholar
  169. Mallahi AE, Minetti C, Dubois F (2013) Automated three-dimensional detection and classification of living organisms using digital holographic microscopy with partial spatial coherent source: application to the monitoring of drinking water resources. Appl Opt 52(1):68–80Google Scholar
  170. Mara D, Horan N (2003) Handbook of water and wastewater microbiology. Academic Press, San DiegoGoogle Scholar
  171. Marimont RB, Shapiro MB (1979) Nearest neighbour searches and the curse of dimensionality. IMA J Appl Math 24(1):59–70zbMATHGoogle Scholar
  172. Markiewicz T, Korzynska A, Kowalski A, Swiderska-Chadaj Z, Murawski P, Grala B, Lorent M, Wdowiak M, Zak J, Roszkowiak L, Kozlowski W, Pijanowska D (2016) MIAP-web-based platform for the computer analysis of microscopic images to support the pathological diagnosis. Biocybern Biomed Eng 36(4):597–609Google Scholar
  173. Mauro RD, Cepeda G, Capitanio F, Vinas MD (2011) Using ZooImage automated system for the estimation of biovolume of copepods from the northern Argentine sea. J Sea Res 66(2):69–75Google Scholar
  174. Mazzoni A, Garcia-Perez E, Zoccolan D, Graziosi S, Torre V (2004) Quantitative characterization and classification of leech behavior. J Neurophysiol 93(1):580–593Google Scholar
  175. Milferstedt K, Pons MN, Morgenroth E (2008) Textural fingerprints: a comprehensive descriptor for biofilm structure development. Biotechnol Bioeng 100(5):889–901Google Scholar
  176. Moon I, Javidi B (2005) Shape tolerant three-dimensional recognition of biological microorganisms using digital holography. Opt Express 13(23):9612–9622Google Scholar
  177. Moon I, Javidi B (2006) Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography. J Biomed Opt 11(6):064,004–1–064,004–7Google Scholar
  178. Moon I, Javidi B (2007) Real time automated three-dimensional recognition of micro/nano biological organisms. In: Proceedings of SPIE 6778, three-dimensional TV, video, and display VI, pp 677,809–1–677,809–9Google Scholar
  179. Moon I, Javidi B (2008) 3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging. IEEE Trans Med Imaging 27(12):1782–1790Google Scholar
  180. Moon I, Daneshpanah M, Javidi B, Stern A (2009) Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy. Proc IEEE 97(6):990–1010Google Scholar
  181. Moon I, Yi F, Javidi B (2010) Automated three-dimensional microbial sensing and recognition using digital holography and statistical sampling. Sensors 10(9):8437–8451Google Scholar
  182. Mosleh MA, Manssor H, Malek S, Milow P, Salleh A (2012) A preliminary study on automated freshwater algae recognition and classification system. BMC Bioinfor 13(Suppl 17):1–13Google Scholar
  183. Motta MD, Pons MN, Vivier H, Amaral AL, Roche ECFN, Mota M (2001) The study of protozoa population in wastewater treatment plants by image analysis. Braz J Chem Eng 18(1) (Online)Google Scholar
  184. Nah W, Baek J (2003) Classification of Caenorhabditis Elegans behavioural phenotypes using an improved binarization method. In: Wang G, Liu Q, Yao Y, Skowron A (eds) Rough sets, fuzzy sets, data mining, and granular computing. Springer, Germany, pp 557–564Google Scholar
  185. Nah W, Hong S, Baek J (2003) Feature extraction for classification of Caenorhabditis Elegans behavioural phenotypes. In: Chung PWH, Hinde C, Ali M (eds) Developments in applied artificial intelligence. Springer, New York, pp 287–295Google Scholar
  186. Neuman U, Korzynska A, Lopez C, Lejeune M, Roszkowiak L, Bosch R (2013) Equalisation of archival microscopic images from immunohistochemically stained tissue sections. Biocybern Biomed Eng 33(1):63–76Google Scholar
  187. Nie D, Shank EA, Jojic V (2015) A deep framework for bacterial image segmentation and classification. In: ACM conference on bioinformatics, computational biology and health informatics, pp 306–314Google Scholar
  188. Nielsen MA (2015) Neural networks and deep learning. Determination Press (Online)Google Scholar
  189. Nogueira PA, Teofilo LF (2014) A multi-layered segmentation method for nucleus detection in highly clustered microscopy imaging: a practical application and validation using human U2OS cytoplasmnucleus translocation images. Artif Intell Rev 42(3):331–346Google Scholar
  190. Nugent C, Cunningham P, Kirwan P (2006) Using active learning to annotate microscope images of parasite eggs. Artif Intell Rev 26(1):63–73Google Scholar
  191. Ochoa D, Gautama S, Vintimilla B (2007) Detection of individual specimens in populations using contour energies. In: Blanc-Talon J, Philips W, Popescu D, Scheunders P (eds) Advanced concepts for intelligent vision systems. Springer, New York, pp 575–586Google Scholar
  192. O’Cleirigh C, Walsh PK, O’Shea DG (2003) Morphological quantification of pellets in Streptomyces hygroscopicus var. geldanus fermentation broths using a flatbed scanner. Biotechnol Lett 25(19):1677–1683Google Scholar
  193. Oh B, Chen Y, Matsuoka H, Yamamoto A, Kurata H (1996) Morphological recognition of fungal spore germination by a computer-aided image analysis and its application to antifungal activity evaluation. J Biotechnol 45(12):71–79Google Scholar
  194. Okafor N (2007) Modern industrial microbiology and biotechnology. Science Publishers, EnfieldGoogle Scholar
  195. Orlov N, Johnston J, Macura T, Shamir L, Goldberg I (2007) Computer vision for microscopy applications. In: Obinata G, Dutta A (eds) Vision systems: segmentation and pattern recognition. I-Tech, Austria, pp 222–242Google Scholar
  196. Osman MK, Mashor MY, Jaafar H (2011a) Hybrid multilayered perceptron network trained by modified recursive prediction error-extreme learning machine for tuberculosis bacilli detection. In: Kuala Lumpur international conference on biomedical engineering, pp 667–673Google Scholar
  197. Osman MK, Mashor MY, Jaafar H (2011b) Tuberculosis bacilli detection in Ziehl–Neelsen-stained tissue using affine moment invariants and extreme learning machine. In: IEEE international colloquium on signal processing and its applications, pp 232–236Google Scholar
  198. Osman MK, Mashor MY, Jaafar H (2012) Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl–Neelsen stained tissue. In: International conference on biomedical engineering, pp 139–143Google Scholar
  199. Pamboukian CRD, Guimaraes LM, Facciotti MCR (2002) Applications of image analysis in the characterization of streptomyces olindensis in submerged culture. Braz J Microbiol 33(1):17–21Google Scholar
  200. Pangilinan C, Divekar A, Coetzee G, Clark DA, Fourie B, Lure FYM, Kennedy S (2011) Application of stepwise binary decision classification for reduction of false positives in tuberculosis detection from smeared slides. In: LASTED international symposia imaging and signal processing in healthcare and technology, pp 1–7Google Scholar
  201. Park JP, Kim YM, Kim SW, Hwang HJ, Cho YJ, Lee YS, Song CH, Yun JW (2002) Effect of aeration rate on the mycelial morphology and exo-biopolymer production in Cordyceps militaris. Process Biochem 37(11):1257–1262Google Scholar
  202. Pasquale FD, Stander J (2009) A multi-scale template method for shape detection with bio-medical applications. Pattern Anal Appl 12(2):179–192MathSciNetGoogle Scholar
  203. Pech-Pacheco JL, Alvarez-Borrego J (1998) Optical-digital system applied to the identification of five phytoplankton species. Mar Biol 132(3):357–365Google Scholar
  204. Pech-Pacheco JL, Alvarez-Borrego J, Cristobal G (2011) Identification of a red tide blooming species through an automatic optical-digital system. In: Proceedings of SPIE 4471, algorithms and systems for optical information processing V, pp 1–8Google Scholar
  205. Pedraza A, Bueno G, Deniz O, Cristobal G, Blanco S, Borrego-Ramos M (2017a) Automated diatom classification (part a): handcrafted feature approaches. Applied Science (in press)Google Scholar
  206. Pedraza A, Bueno G, Deniz O, Cristobal G, Blanco S, Borrego-Ramos M (2017b) Automated diatom classification (part b): a deep learning approach. Appl Sci 7(5):1–25Google Scholar
  207. Pepper IL, Gerba CP, Gentry TJ (2014) Environmental microbiology, 3rd edn. Academic Press, LondonGoogle Scholar
  208. Perner P (2006) Similarity-based object recognition of airborne fungi in digital images. In: Bucchianico AD, Mattheij RMM, Peletier MA (eds) Progress in industrial mathematics at ECMI 2004. Springer, New York, pp 325–329Google Scholar
  209. Perner P, Perner H, Jaenichen S, Buehring A (2004) Recognition of airborne fungi spores in digital microscopic images. In: International conference on pattern recognition, pp 566–569Google Scholar
  210. Pichon D, Vivier H, Pons MN, Lounes A, Lebrihi A (1994) Characterization and growth monitoring of filamentous microorganisms by image analysis. ACTA Stereol 13(1):215–220Google Scholar
  211. Prabhu A, Wadekar M (2010) CMEIAS sampling statistics. Report in Michigan State University MTH 844Google Scholar
  212. Priya E, Srinivasan S (2015a) Automated identification of tuberculosis objects in digital images using neural network and neuro fuzzy inference systems. J Med Imaging Health Inf 5(3):506–512Google Scholar
  213. Priya E, Srinivasan S (2015b) Separation of overlapping bacilli in microscopic digital TB images. Biocybern Biomed Eng 35(2):87–99Google Scholar
  214. Priya E, Srinivasan S (2016) Automated object and image level classification of TB images using support vector neural network classifier. Biocybern Biomed Eng 36(4):670–678Google Scholar
  215. Priya E, Srinivasan S, Ramakrishnan S (2012) Classification of tuberculosis digital images using hybrid evolutionary extreme learning machines. Technologies and applications. In: Nguyen N, Hoang K, Jedrzejowicz P (eds) Computational collective intelligence. Springer, Berlin, pp 268–277Google Scholar
  216. Promdaen S, Wattuya P, Sanevas N (2014) Automated microalgae image classification. Procedia Comput Sci 29:1982–1992Google Scholar
  217. Qi S, Meesters S, Nicolay K, Romeny BMTH, Ossenblok P (2015) The Influence of construction methodology on structural brain network measures: a review. J Neurosci Methods 253:170–182Google Scholar
  218. Qi S, Meesters S, Nicolay K, Romeny BMTH, Ossenblok P (2016) Structural brain network: what is the effect of life optimization of whole brain tractography? Front Comput Neurosci 10:1–12Google Scholar
  219. Rangaswami G, Bagyaraj DJ (2004) Agricultural microbiology. Prentice-Hall of India Pvt. Ltd., Englewood CliffsGoogle Scholar
  220. Reichl U, King R, Gilles ED (1992) Characterization of pellet morphology during submerged growth of streptomyces tendae by image analysis. Biotechnol Bioeng 39:164–170Google Scholar
  221. Rodenacker K, Gais P, Jutting U, Hense BA (2001) (Semi-) automatic recognition of microorganisms in water. In: International conference on image processing, pp 30–33Google Scholar
  222. Rodenacker K, Gais P, Juetting U, Hense BA (2002) Identification and quantification of phytoplankton by image analysis. GSF-Report 02/02, 16-24, ISSN 0721-1694. Neuherberg, GermanyGoogle Scholar
  223. Rodriguez A, Guil N, Shotton DM, Trelles O (2004) Automatic analysis of the content of cell biological videos and database organization of their metadata descriptors. IEEE Trans Multimed 6(1):119–128Google Scholar
  224. Ronen M, Guterman H, Shabtai Y (2002) Monitoring and control of pullulan production using vision sensor. J Biochem Biophys Methods 51(3):243–249Google Scholar
  225. Ruehl M, Kuees U (2009) Automated image analysis to observe pellet morphology in liquid cultures of filamentous fungi such as the basidiomycete Coprinopsis cinerea. Curr Trends Biotechnol Pharmacy 3(3):241–253Google Scholar
  226. Rulaningtyas R, Suksmono AB, Mengko TLR (2011) Automatic classification of tuberculosis bacteria using neural network. In: International conference on electrical engineering and informatics, pp 1–4Google Scholar
  227. Ruusuvuori P, Seppala J, Erkkila T, Lehmussola A, Puhakka JA, Yli-Harja O (2008) Efficient automated method for image-based classification of microbial cells. In: International conference on pattern recognition, pp 1–4Google Scholar
  228. Sadaphal P, Rao J, Comstock GW, Beg MF (2008) Image processing techniques for identifying mycobacterium tuberculosis in Ziehl–Neelsen stains. Int J Tuberc Lung Dis 12(5):579–582Google Scholar
  229. Santhi N, Pradeepa C, Subashini P, Kalaiselvi S (2013) Automatic identification of algal community from microscopic images. Bioinform Biol Insights 7:327–334Google Scholar
  230. Schaap A, Rohrlack T, Bellouard Y (2012) Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition. In: Proceedings of SPIE 8251, microfluidics, BioMEMS, and medical microsystems X, pp 825,104–1–825,104–10Google Scholar
  231. Schulze K, Tillich UM, Dandekar T, Frohme M (2013) PlanktoVision—an automated analysis system for the identification of phytoplankton. BMC Bioinform 14(115):1–10Google Scholar
  232. Schusterreiter C (2011) Computational analysis of drosophila courtship behaviour. PhD Dissertation in Universitaet WienGoogle Scholar
  233. Senthilkumaran N, Rajesh R (2009) Edge detection techniques for image segmentation—a survey of soft computing approaches. Int J Recent Trends Eng 1(2):250–254Google Scholar
  234. Shabtai Y, Ronen M, Muknenev I, Guterman H (1996) Monitoring micorbial morphogenetic changes in a fermentation process by a self-tuning vision system (STVS). Pergamon 20(1):321–326Google Scholar
  235. Shen M, Szyszkay P, Galiziay CG, Merhof D (2013) Automatic framework for tracking honeybee’s antennae and mouthparts from low framerate video. In: International conference on image processing, pp 4112–4116Google Scholar
  236. Shen M, Huang W, Szyszkay P, Merhof D (2014) Interactive framework for insect tracking with active learning. In: International conference on pattern recognition, pp 2733–2738Google Scholar
  237. Shen M, Li C, Huang W, Szyszka P, Shirahama K, Grzegorzek M, Merhof D, Duessen O (2015a) Interactive tracking of insect posture. Pattern Recogn 48(11):3560–3571Google Scholar
  238. Shen M, Szyszkay P, Deussen O, Galiziay CG, Merhof D (2015b) Automated tracking and analysis of behaviour in restrained insects. J Neurosci Methods 239:194–205Google Scholar
  239. Shirahama K, Li C, Grzegorzek M, Uehara K (2013) University of Siegen, Kobe University and Muroran Institute of Technology at TRECVID 2013 multimedia event detection. In: TRECVID 2013 multimedia event detection (Online)Google Scholar
  240. Shotton DM, Rodriguez A, Guil N, Trelles O (2000) Object tracking and event recognition in biological microscopy videos. In: International conference on pattern recognition, pp 226–229Google Scholar
  241. Sieracki ME, Webb LK (1991) The application of image analysed fluorescence microscopy for characterising planktonic bacteria and protists. In: Reid PC, Turley CM, Burkill PH (eds) Protozoa and their role in marine processes. Springer, New York, pp 77–100Google Scholar
  242. Sklarczyk C, Perner H, Rieder H, Arnold W, Perner P (2007) Image acquisition and analysis of hazardous biological material in air. In: Carbonell JG, Siekmann J (eds) Advances in mass data analysis of signals and images in medicine biotechnology and chemistry. Springer, New York, pp 1–14Google Scholar
  243. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380Google Scholar
  244. Snoek CGM, Worring M, Smeulders AWM (2005) Early versus late fusion in semantic video analysis. In: ACM international conference on multimedia, pp 399–402Google Scholar
  245. Soda P, Iannello G, Vento M (2009) A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis. Pattern Anal Appl 12(3):215–226MathSciNetGoogle Scholar
  246. Solomon CJ, Breckon TP (2010) Fundamentals of digital image processing: a practical approach with examples in matlab. Wiley-Blackwell, ChichesterGoogle Scholar
  247. Sosik HM, Olson RJ (2007) Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol Oceanogr Methods 5(6):204–216Google Scholar
  248. Suri JS, Singh S, Reden L (2002) Computer vision and pattern recognition techniques for 2-D and 3-D MR cerebral cortical segmentation (part I): a state-of-the-art review. Pattern Anal Appl 5(1):46–76MathSciNetGoogle Scholar
  249. Suzuki CTN, Gomes JF, Falcao AX, Papa JP, Hoshino-Shimizu S (2013a) Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Trans Biomed Eng 60(3):803–812Google Scholar
  250. Suzuki CTN, Gomes JF, Falcao AX, Shimizu SH, Papa JP (2013b) Automated diagnosis of human intestinal rarasites using optical microscopy images. In: IEEE international symposium on biomedical imaging, pp 460–463Google Scholar
  251. Tamura S, Park Y, Toriyama M, Okabe M (1997) Change of mycelial morphology in tylosin production by batch culture of streptomyces fradiae under various shear conditions. J Ferment Bioeng 83(6):523–528Google Scholar
  252. Tang X, Stewart WK (1996) Plankton image classification using novel parallel-training learning vector quantization network. In: OCEANS’96. MTS/IEEE. Prospects for the 21st Century, pp 1227–1236Google Scholar
  253. Tang X, Stewart WK, Vincent L, Huang H, Marra M, Gallager SM, Davis CS (1998) Automatic plankton image recognition. In: Panigrahi S, Ting KC (eds) Artificial intelligence for biology and agriculture. Kluwer Academic Publishers, Dordrecht, pp 177–199Google Scholar
  254. Tang X, Lin F, Samson S, Remsen A (2006) Binary plankton image classification. IEEE J Ocean Eng 31(3):728–735Google Scholar
  255. Tao J, Cheng W, Wang B, Xie J, Jiao N, Luo T (2008) Real-time red tide algae classification using Naive Bayes classifier and SVM. In: International conference on bioinformatics and biomedical engineering, pp 2888–2891Google Scholar
  256. Tao J, Cheng W, Wang B, Xie J, Jiao N, Luo T (2010) Real-time red tide algae recognition using SVM and SVDD. In: IEEE international conference on intelligent computing and intelligent systems, pp 602–606Google Scholar
  257. Tasoulis SK, Maglogiannis I, Plagianakos VP (2014) Fractal analysis and fuzzy C-means clustering for quantification of fibrotic microscopy images. Artif Intell Rev 42(3):313–329Google Scholar
  258. Tautenhahn R, Ihlow A, Seiffert U (2006) Adaptive feature selection for classification of microscope images. In: Bloch I, Petrosino A, Tettamanzi AGB (eds) Fuzzy logic and applications. Springer, New York, pp 215–222Google Scholar
  259. Tdth LG, Kato K (1997) Size-selective grazing of bacteria by bosmina longirostris-an image-analysis study. J Plankton Res 19(10):1477–1493Google Scholar
  260. Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Elsevier, New YorkzbMATHGoogle Scholar
  261. Thiel S, Davies RJWLJ (1995) Automated object recognition of blue–green algae for measuring water quality—a preliminary study. Water Res 29(10):2398–2404Google Scholar
  262. Thiel S, Wiltshire RJ (1995) The automated detection of cyanobacteria using ddigital image processing techniques. Environ Int 21(2):233–236Google Scholar
  263. Thonnat M, Gandelin MH (1988) An expert system for the automatic classification and description of zooplanktons from monocular images. In: IEEE international conference on pattern recognition, pp 114–118Google Scholar
  264. Trujillo O, Griffis C, Li Y, Slavik M (2001) A machine vision system using immuno-fluorescence microscopy for rapid recognition of salmonella typhimurium. J Rapid Methods Autom Microbiol 9(2):115–134Google Scholar
  265. Truquet P, Lassiis P, Honsell G, Dean LL (1996) Application of a digital pattern recognition system to Dinophysis acuminata and D-sacculus complexes. Aquat Living Resour 9(3):273–279Google Scholar
  266. Tsnji T, Nishikawa T (1984) Automated identification of red tide phytoplankton prorocentrum triestinum in coastal areas by image analysis. J Oceanogr Soc Jpn 40(6):425–431Google Scholar
  267. Tuzel O, Yang L, Meer P, Foran DJ (2007) Classification of hematologic malignancies using texton signatures. Pattern Anal Appl 10(4):277–290MathSciNetGoogle Scholar
  268. Uhlmann D, Schlimpeet O, Uhlmann W (1978) Automated phytoplankton analysis by a pattern recognition method. Int Rev Hydrobiol 63(4):575–583Google Scholar
  269. Vantaram SR, Saber E (2012) Survey of contemporary trends in colour image segmentation. J Electron Imaging 21(4):040,901-1–040,901-28Google Scholar
  270. Vapnik VN (1998) Statistical learning theory. Wiley-Interscience, New YorkzbMATHGoogle Scholar
  271. Vater SM, Weisse S, Maleschlijski S, Lotz C, Koschitzki F, Schwartz T, Obst U, Rosenhahn A (2014) Swimming behavior of pseudomonas aeruginosa studied by holographic 3D tracking. PLoS ONE 9(1):1–11Google Scholar
  272. Verikas A, Gelzinis A, Bacauskiene M, Olenina I, Vaiciukynas E (2015) An integrated approach to analysis of phytoplankton images. IEEE J Ocean Eng 40(2):315–326Google Scholar
  273. Veropoulos K, Campbell C, Learmonth G (1998) Image processing and neural computing used in the diagnosis of tuberculosis. In: IEE colloquium on intelligent methods in healthcare and medical applications, pp 8/1–8/4Google Scholar
  274. Walker RF, Kumagai M (2000) Image analysis as a tool for quantitative phycology: a computational approach to cyanobacterial taxa identification. Limnology 1(2):107–115Google Scholar
  275. Wang D, Wang B, Yan Y (2013) The Identification of powdery mildew spores image based on the integration of intelligent spore image sequence capture device. In: International conference on intelligent information hiding and multimedia signal processing, pp 177–180Google Scholar
  276. Wang G, Kalra M, Orton CG (2017) Machine learning will transform radiology significantly within the next 5 years. Med Phys 44(6):2041–2044Google Scholar
  277. Wang J, Trubuil A, Graffigne C (2001) 3D Biological object detection and labeling in multidimensional microscopy imaging. In: International conference on image analysis and processing, pp 215–220Google Scholar
  278. Wang J, Trubuil A, Graffigne C, Kaeffer B (2003) 3-D aggregated object detection and labeling from multivariate confocal microscopy images: a model validation approach. IEEE Trans Syst Man Cybern Part B Cybern 33(4):572–581Google Scholar
  279. Wang L, Yang B, Abraham A, Qi L, Zhao X, Chen Z (2014) Construction of dynamic three-dimensional microstructure for the hydration of cement using 3D image registration. Pattern Anal Appl 17(3):655–665MathSciNetGoogle Scholar
  280. Watson J (2000) Subsea holography and its application in marine science. In: Proceedings of the EurOCEAN 2000 conference, pp 271–272Google Scholar
  281. Weller AF, Harris AJ, Ware JA (2007) Two supervised neural networks for classification of sedimentary organic matter images from palynological preparations. Math Geol 39(7):657–671Google Scholar
  282. Widmer KW, Srikumar D, Pillai SD (2005) Use of artificial neural networks to accurately identify cryptosporidium oocyst and giardia cyst images. Appl Environ Microbiol 71(1):80–84Google Scholar
  283. Wit P, Busscher HJ (1998) Application of an artificial neural network in the enumeration of yeasts and bacteria adhering to solid substrata. J Microbiol Methods 32(3):281–290Google Scholar
  284. Witkowski L (2013) A computer system for a human semen quality assessment. Biocybern Biomed Eng 33(3):179–186MathSciNetGoogle Scholar
  285. Wu S, Jiang T, Zhang G, Schoenemann B, Nert F, Zhu M, Bu C, Han J, Kuhnert K (2016) Artificial compound eye: a survey of the state-of-the-art. Artif Intell Rev pp 1–31.
  286. Xu N (2016) A comparative study of female-themed art films from China and Germany. Logos Verlag Berlin GmbH, BerlinGoogle Scholar
  287. Yamaguchi N, Ichijo T, Ogawa M, Tanji K, Nasu M (2004) Multicolor excitation direct counting of bacteria by fluorescence microscopy with the automated digital image analysis software BACS II. Bioimages 12(1):1–7Google Scholar
  288. Yang C, Li C, Tiebe O, Shirahama K, Grzegorzek M (2014) Shape-based classification of environmental microorganisms. In: International conference on pattern recognition, pp 3374–3379Google Scholar
  289. Yang K, Wang J, Li X, Feng X, Duan S (2001) Strain selection of metarrhizium anisopliae by image analysis of colony morphology for consistency of steroid biotransformation. Biotechnol Bioeng 75(1):53–62Google Scholar
  290. Yang M, Kpalma K, Ronsin J (2008) A Survey of shape feature extraction techniques. In: Yin P (ed) Pattern recognition. IN-TECH, pp 43–90Google Scholar
  291. Yang X, Beyenal H, Harkin G, Lewandowski Z (2000) Quantifying biofilm structure using image analysis. J Microbiol Methods 39(2):109–119Google Scholar
  292. Yang YK, Morikawa M, Shimizu H, Shioya S, Suga K, Nihira T, Yamada Y (1996) Image analysis of mycelial morphology in virginiamycin production by batch culture of Streptomyces virginiae. J Ferment Bioeng 81(1):7–12Google Scholar
  293. Yao J, Kharma N, Grogono P (2005) A multi-population genetic algorithm for robust and fast ellipse detection. Pattern Anal Appl 8(1–2):149–162MathSciNetGoogle Scholar
  294. Ye L, Chang C, Hsieh C (2011) Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation. Mar Ecol Prog Ser 441(15):185–196Google Scholar
  295. Yeom S, Javidi B (2006) Automatic identification of biological microorganisms using three-dimensional complex morphology. J Biomed Opt 11(2):024017-1–024017-8Google Scholar
  296. Yeom S, Moon I, Javidi B (2006) Real-time 3-D sensing, visualization and recognition of dynamic biological microorganisms. Proc IEEE 94(3):550–566Google Scholar
  297. Yeom S, Moon I, Javidi B (2007) Two approaches to 3D microorganism recognition using single exposure online (SEOL) digital holography. In: Sadjadi F, Javidi B (eds) Physics of automatic target recognition. Springer, New York, pp 175–194Google Scholar
  298. Yu B, Elbuken C, Ren CL, Huissoon JP (2011) Image processing and classification algorithm for yeast cell morphology in a microfluidic chip. J Biomed Opt 16(6):1–9Google Scholar
  299. Zalewski K, Buchholz R (1996) Morphological analysis of yeast cells using an automated image processing system. J Biotechnol 48(1–2):43–49Google Scholar
  300. Zalewski K, Gotz P, Buchholz R (1994) On-line estimation of yeast growth rate using morphological data from image analysis. In: Galindo E, Ramirez OT (eds) Advances in bioprocess engineering. Springer, New York, pp 191–195Google Scholar
  301. Zeder M, Kohler E, Pernthaler J (2010) Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria. Cytom Part A 77(A):76–85Google Scholar
  302. Zetsche E, Mallahi AE, Dubois F, Yourassowsky C, Kromkamp JC, Meysman FJR (2014) Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms. Limnol Oceanogr Methods 12:757–775Google Scholar
  303. Zhang D, Lu G (2004) Review of Shape representation and description techniques. Pattern Recogn 37(1):1–19Google Scholar
  304. Zhang J, Chen Y, Bekkers E, Wang M, ter Haar Romeny BM, Dashtbozorg B (2017) Retinal vessel delineation using a brain-inspired wavelet transform and random forest. Pattern Recogn 69:107–123Google Scholar
  305. Zhang T, Jia W, Zhu Y, Yang J (2016) Automatic tracking of neural stem cells in sequential digital images. Biocybern Biomed Eng 36(1):66–75Google Scholar
  306. Zhao F, Tang X, Lin F, Samson S, Remsen A (2005) Binary plankton image classification using random subspace. In: IEEE international conference on image processing, pp 357–360Google Scholar
  307. Zhao F, Lin F, Seah HS (2010) Binary SIPPER plankton image classification using random subspace. Neurocomputing 73(10–12):1853–1860Google Scholar
  308. Zhao X, Xing D, Fu N, Liu B, Ren N (2011) Hydrogen production by the newly isolated Clostridium beijerinckii RZF-1108. Bioresour Technol 102(18):8432–8436Google Scholar
  309. Zhao X, Xing D, Liu B, Lua L, Zhao J, Ren N (2012) The effects of metal ions and L-cysteine on HydA gene expression and hydrogen production by Clostridium beijerinckii RZF-1108. Int J Hydrog Energy 37(18):13,711–13,717Google Scholar
  310. Zhao X, Li D, Xu S, Guo Z, Zhang Y, Man L, Jiang B, Hu X (2017) Clostridium guangxiense sp. nov. and Clostridium neuense sp. nov., two phylogenetically closely related hydrogen-producing species isolated from lake sediment. Int J Syst Evol Microbiol 67(7):710–715Google Scholar
  311. Zhou B, Baek J (2006) An automatic nematode identification method based on locomotion patterns. In: Huang D, Li K, Irwin GW (eds) Computational intelligence and bioinformatics. Springer, New York, pp 372–380Google Scholar
  312. Zhou B, Hah W, Lee K, Baek J (2005) A general image based nematode identification system design. In: Hao Y, Liu J, Wang Y, Cheung Y, Yin H, Jiao L, Ma J, Jiao Y (eds) Computational intelligence and security. Springer, New York, pp 899–904Google Scholar
  313. Zhou H, Wang C, Wang R (2008) Biologically-inspired identification of plankton based on hierarchical shape semantics modeling. In: International conference on bioinformatics and biomedical engineering, pp 2000–2003Google Scholar
  314. Zou Y, Li C, Boukhers Z, Jiang T, Shirahama K, Grzegorzek M (2015) Environmental microbiological content-based image retrieval system using internal structure histogram. In: International conference on computer recognition systems, pp 543–552Google Scholar
  315. Zou Y, Li C, Shirahama K, Jiang T, Grzegorzek M (2016a) Content-based microscopic image retrieval of environmental microorganisms using multiple colour channels fusion. In: Lee R (ed) Computer and information science. Springer, New York, pp 119–130Google Scholar
  316. Zou Y, Li C, Shirahama K, Jiang T, Grzegorzek M (2016b) Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation. In: IEEE International conference on image processing, pp 2475–2479Google Scholar
  317. Zou Y, Chen LC, Shirahama K, Tao JC, Grzegorzek M (2017) Content-based image retrieval of environmental microorganisms using double-stage optimisation-based fusion. Inf Eng Express (in press)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Chen Li
    • 1
  • Kai Wang
    • 2
  • Ning Xu
    • 3
  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  3. 3.School of Arts and DesignLiaoning Shihua UniversityFushunChina

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