Automated clustering of lignocellulosic fibres based on morphometric features and using clustering of variables

Industrial Crops and Products

Volumn 45, Issue , 2013, Pages 253-261

  Legland, D ^a   Beaugrand, J ^b,c  

^a UMR SayFood   (France)

^b UMR614 FARE   (France)

^c UNIVERSITÉ DE REIMS CHAMPAGNE ARDENNE   (France)

Author keywords

Clustering; Feature selection; Fibre morphology; Image analysis; Morphometric feature; Tortuosity

Indexed keywords

ACQUISITION DEVICE; AUTOMATED CLUSTERING; BRANCHING STRUCTURES; CLUSTERING; CLUSTERING APPROACH; DESCRIPTORS; DESIGN OF COMPOSITES; DIGITAL IMAGE; FIBRE MORPHOLOGY; GENERIC PROCEDURES; HIGH RESOLUTION; IN-DEPTH INVESTIGATION; MORPHOMETRIC FEATURE; PARTICLE ANALYSIS; TORTUOSITY;

FEATURE EXTRACTION; IMAGE ANALYSIS; MECHANICAL PROPERTIES; MORPHOLOGY;

FIBERS;

AUTOMATION; CELLULOSE; CLUSTER ANALYSIS; COMPOSITE; DIGITAL IMAGE; IMAGE ANALYSIS; MORPHOMETRY; TYPOLOGY;

EID: 84872544804     PISSN: 09266690     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.indcrop.2012.12.021     Document Type: Article

References (60)

1. Alila S., Besbes I., Vilar M.R., Mutjé P., Boufi S. Non-woody plants as raw materials for production of microfibrillated cellulose (MFC): a comparative study. Ind. Crop Prod. 2013, 41:250-259.
2. Beaugrand, J., Berzin, F., 2012. Lignocellulosic fiber reinforced composites: influence of compounding conditions on defibrization and mechanical properties. J. Appl. Polym. Sci., doi:10.1002/APP.38468.
3. Bénézet J.-C., Stanojlovic-Davidovic A., Bergeret A., Ferry L., Crespy A. Mechanical and physical properties of expanded starch, reinforced by natural fibres. Ind. Crop Prod. 2012, 37(1):435-440.
4. Borgefors G. Distance transformations in digital images. Comput. Vis. Graph. Image Process. 1986, 34:344-371.
5. Burger W., Burge M.J. Digital Image Processing, An Algorithmic Introduction using Java 2008, Springer-Verlag, New York, NY, USA.
6. Carvalho M.G., Ferreira P.J., Martins A.A., Figueiredo M.M. A comparative study of two automated techniques for measuring fibre length. Tappi J. 1997, 80(2):137-142.
7. Chavent M., Kuetz V., Liquet B., Saracco J. ClustOfVar: an R package for the clustering of variables. J. Stat. Softw. 2012, 50(13):1-16.
8. Chitwood D.H., Headland L.R., Kumar R., Peng J., Maloof J.N., Sinha N.R. The developmental trajectory of leaflet morphology in wild tomato species. Plant Physiol. 2012, 158:1230-1240.
9. Costa C., Menesatti P., Paglia G., Pallottino F., Aguzzi J., Rimatori V., Russo G., Recupero S., Recupero G.R. Quantitative evaluation of Tarocco sweet orange fruit shape using optoelectronic elliptic Fourier based analysis. Postharvest Biol. Technol. 2009, 54:38-47.
10. Coster M., Chermant J.-L. Précis d'analyse d'images 1989, Presses du CNRS.
11. Diao X.-M., Furuno T., Fujita M. Digital image analysis of cross-sectional tracheid shapes in Japanese softwoods using the circularity index and aspect ratio. J. Wood Sci. 1999, 45:98-105.
12. Eymin Petot Tourtollet G., Gillet G., Lafaverges P., Kerneis J.C., Sabater J., Voillot C., Passas R., Pascal R. A new tool for pulp morphology analysis. Measurement and Control of Papermaking Conference 2000, 1-13. The Caledonian Hilton, Edinburgh.
13. Faruk O., Bledzki A.K., Fink H.P., Sain M. Biocomposites reinforced with natural fibers: 2000-2010. Prog. Polym. Sci. 2012, 37(11):1552-1596.
14. Frangi A.F., Niessen W.J., Vincken K.L., Viergever M.A. Multiscale vessel enhancement filtering. MICCAI'98 Medical Image Computing and Computer-assisted Intervention Lecture Notes in Computer Science 1998, 130-137. W.M. Wells, A. Colchester, S. Delp (Eds.).
15. Grishanov S.A., Harwood R.J., Booth I. A method of estimating the single flax fibre fineness using data from the LaserScan system. Ind. Crop Prod. 2006, 23:273-287.
16. Guay D., Sutherland N.R., Rantanen W., Malandri N., Stephens A., Mattingly K., Schneider M. Comparison of fiber length analyzers. TAPPI Practical Papermaking Conference Proceedings 2005.
17. Guillemin F., Devaux M.-F., Guillon F. Evaluation of plant histology by automatic clustering based on individual cell morphological features. Image Anal. Stereol. 2004, 23:13-22.
18. Guillon F., Sully P., Bouchet B., Devaux M.-F., Frasse P., Jones B., Bouzayen M., Lahaye M. Down-regulation of an Auxin Response Factor in the tomato induces modification of fine pectin structure and tissue architecture. J. Exp. Bot. 2008, 59(2):273-288.
19. Hirn U., Bauer W. A review of image analysis based methods to evaluate fiber properties. Lenzinger Berichte 2006, 86:96-105.
20. Holbery J., Houston D. Natural-fiber-reinforced polymer composites in automotive applications. J. Miner. Metals Mater. Soc. 2006, 58(11):80-86.
21. Impoco G., Fucà N., Pasta C., Caccamo M., Licitra G. Quantitative analysis of nanostructures shape and distribution in micrographs using image analysis. Comput. Electron. Agric. 2012, 84:26-35.
22. Iwata H., Niikura S., Matsuura S., Takano Y., Ukai Y. Evaluation of variation of root shape of Japanese radish (Raphanus sativus L.) based on image analysis using elliptic Fourier descriptors. Euphytica 1998, 102:143-149.
23. Jarabo R., Fuente E., Monte M.C., Savastano H., Mutjé P., Negro C. Use of cellulose fibers from hempcore in fiber-cement production. Effect on flocculation, retention, drainage and product properties. Ind. Crop Prod. 2012, 39:89-96.
24. Johnson M., Tucker N., Barnes S., Kirwan K. Improvement of the impact performance of a starch based biopolymer via the incorporation of Miscanthus giganteus fibres. Ind. Crop Prod. 2005, 22(3):175-186.
25. Joliffe I. Principal Components Analysis. Springer Series in Statistics 2002, Springer-Verlag. 2nd ed.
26. Kaufman L., Rousseeuw P. Finding Groups in Data: An Introduction to Cluster Analysis 1990, John Wiley & Sons, Hoboken, NJ, USA.
27. Konstankiewicz K., Pawlak K., Zdunek A. Quantitative method for determining cell structural parameters of plant tissues. Int. Agrophys. 2001, 16:161-164.
28. Lantuéjoul C., Beucher S. On the use of geodesic metric in image analysis. J. Miscrosc. 1981, 121:39-40.
29. Le Moigne N., van den Oever M., Budtova T. A statistical analysis of fibre size and shape distribution after compounding in composites reinforced by natural fibres. Compos. Part A 2011, 42:1542-1550.
30. Legland, D., Devaux, M.-F., 2009. Détection semi-automatique de cellules de fruits charnus observés par microscopie confocale 2D et 3D. Cahiers techniques de l'INRA, Special Issue on Imagery 7-16. Available online: http://www.inra.fr/content/download/18562/289936/version/1/file/07_Legland_imag.pdf.
31. Lehmann G., Legland D. Efficient N-dimensional surface estimation using Crofton formula and run-length encoding. Insight J. 2012, Available online: http://www.insight-journal.org/browse/publication/852.
32. Leschonski K. Representation and evaluation of particle size analysis data. Part. Part. Syst. Char. 1984, 1(1-4):89-95.
33. Lootens P., Waes J.V., Carlier L. Description of the morphology of roots of Chicorium intybus L. partim by means of image analysis: comparison of elliptic Fourier descriptors and classical parameters. Comput. Electron. Agric. 2007, 58:164-173.
34. Lu J.Z., Monlezun C.J., Wu Q., Cao Q. Fitting Weibull and lognormal distributions to medium-density fiberboard fiber and wood particle length. Wood Fiber Sci. 2007, 39(1):82-94.
35. MacQueen J.B. Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1 1967, 281-297. University of California Press, Berkeley.
36. Montaño-Leyva B., Ghizzi D., da Silva G., Gastaldi E., Torres-Chávez P., Gontard N., Angellier-Coussy H. Biocomposites from wheat proteins and fibers: structure/mechanical properties relationships. Ind. Crop Prod. 2013, 43:545-555.
37. Moral A., Concepcion Monte M., Cabeza E., Blanco A. Morphological characterisation of pulps to control paper properties. Cell. Chem. Technol. 2010, 44(10):473-480.
38. Ntenga R., Beakou A. Structure, morphology and mechanical properties of Rhectophyllum camerunense (RC) plant-fiber. Part I: statistical description and image-based reconstruction of the cross-section. Comput. Mater. Sci. 2011, 50:1442-1449.
39. Pauwels E.J., de Zeeuw P.M., Ranguelova E.B. Computer-assisted tree taxonomy by automated image recognition. Eng. Appl. Artif. Intell. 2009, 22:26-31.
40. Perea-Flores M.J., Chanona-Pérez J.J., Garibay-Febles V., Calderón-Dominguez G., Mendoza-Pérez Terrés-Rojas E., Herrera-Bucio, R J.A. Microscopy techniques and image analysis for evaluation of some chemical and physical properties and morphological features for seeds of the castor oil plant (Ricinus communis). Ind. Crop Prod. 2011, 34(1):1057-1065.
41. Pickering K.L., Beckermann G.W., Alam S.N., Foreman N.J. Optimising industrial hemp fibre for composites. Compos. Part A: Appl. Sci. Manuf. 2007, 38(2):461-468.
42. Pieczywek P.M., Zdunek A. Automatic classification of cells and intercellular spaces of apple tissue. Comput. Electron. Agric. 2012, 81:72-78.
43. Pincus Z., Theriot J. A comparison of quantitative methods for cell-shape analysis. J. Microsc. 2007, 227:140-156.
44. Pirard E., Dislaire G. Sensitivity of particle size and shape parameters with respect to digitization. 13th International Congress for Stereology 2011.
45. Robertson G., Olson J., Allen P., Chan B., Seth R. Measurement of fiber length, coarseness, and shape with the fiber quality analyzer. Tappi J. 1999, 82(10):93-98.
46. Russ J.C. The Image Processing Handbook 2006, CRC Press, Boca Raton, FL, USA.
47. Sandau K., Ohser J. The chord length transform and the segmentation of crossing fibers. J. Microsc. 2007, 226:43-53.
48. Schmid H.G., Müssig J. Characterisation of polyamide fibre width using quantitative image analysis. Melliand Textilberichte-International Textile Reports 2004, 10:E110.
49. Shon K., White J. A comparative study of fiber breakage in compounding glass fiber-reinforced thermoplastics in a Buss Kneader, modular co-rotating and counter-rotating twin screw extruders. Polym. Eng. Sci. 1999, 39:1757-1768.
50. Shouche S.P., Radtogi R., Bhagwat S.G., Sainis J.K. Shape analysis of grains of Indian wheat varieties. Comput. Electron. Agric. 2001, 33(1):55-76.
51. Silva M.C., Lopes O.R., Colodette J.L., Porto A.O., Rieumont J., Chaussy D., Belgacem M.N., Silva G.G. Characterization of three non-product materials from a bleached eucalyptus kraft pulp mill, in view of valorising them as a source of cellulose fibres. Ind. Crop Prod. 2008, 27(3):288-295.
52. Smýkalová I., Hampel D., Pavelek M. Determination of duplicates of accessions in a germplasm collection of flax/linseed by means of digital image analysis. Ind. Crop Prod. 2012, 36(1):177-187.
53. Soille P. Morphological Image Analysis - Principles and Applications 2003, Springer-Verlag, Berlin, Heidelberg, New York, (corr. 2nd printing). 2nd ed.
54. Steger, C., 1996. An Unbiased Detector of Curvilinear Structures. Technical report, Technische Universität München.
55. Thiel, E., 1994. Les distances de chanfrein en analyse d'images: fondements et applications. PhD Thesis. Université Joseph Fourier-Grenoble.
56. Trepanier R.J. Automatic fiber length and shape measurement by image analysis. Tappi J. 1998, 81(6):152-154.
57. Vigneau E., Qannari E.M. Clustering of variables around latent components. Commun. Stat. Simul. Comput. 2003, 32(4):1131-1150.
58. Wang H., Mahar T., Liu X., Wang X. Measurement precision and evaluation of the diameter profiles of single wool fibres. Text. Res. J. 2007, 77(12):981-991.
59. Yoshihara N. Adjusting the lengths of glass fibers and the relationship between fiber length and mechanical properties for reinforced polyethylene terephthalate. J. Polym. Eng. 2006, 26(6):547-564.
60. Zapotoczny P. Discrimination of wheat grain varieties using image analysis: morphological features. Eur. Food Res. Technol. 2011, 233:769-779.