Automated pollen identification using microscopic imaging and texture analysis

Micron

Volumn 68, Issue , 2015, Pages 36-46

  Marcos, J Víctor ^a   Nava, Rodrigo ^b   Cristóbal, Gabriel ^a   Redondo, Rafael ^a,c   Escalante Ramírez, Boris ^b   Bueno, Gloria ^c   Déniz, Óscar ^c   González Porto, Amelia ^d   Pardo, Cristina ^e   Chung, François ^f   Rodríguez, Tomás ^f  

^a CSIC   (Spain)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^c UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^d Centro Agra Rio de Marchamalo   (Spain)

^e UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^f Tecnologías Avanzadas Inspiralia ITAV   (Spain)

Author keywords

Discrete tchebichef moments; Gray level co occurrence matrix; Local binary patterns; Log Gabor filters; Pollen identification; Texture analysis

Indexed keywords

DIMENSIONALITY REDUCTION; DISCRIMINANT ANALYSIS; GABOR FILTERS; IMAGE SEGMENTATION; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; TEXTURES;

GRAY LEVEL CO-OCCURRENCE MATRIX; LOCAL BINARY PATTERNS; LOG-GABOR FILTER; TCHEBICHEF MOMENTS; TEXTURE ANALYSIS;

IMAGE TEXTURE;

CLASSIFICATION; IMAGE PROCESSING; LABORATORY AUTOMATION; MICROSCOPY; PHYSICAL CHEMISTRY; POLLEN; PROCEDURES; SURFACE PROPERTY;

AUTOMATION, LABORATORY; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROSCOPY; PHYSICOCHEMICAL PHENOMENA; POLLEN; SURFACE PROPERTIES;

EID: 84907459399     PISSN: 09684328     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.micron.2014.09.002     Document Type: Article

References (41)

1. Bishop C.M. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford.
2. Boucher A., Hidalgo P.J., Thonnat M., Belmonte J., Galan C., Bonton P., Tomczak R. Development of a semi-automatic system for pollen recognition. Aerobiologia 2002, 18:195-201.
3. Carrión P., Cernadas E., Gálvez J.F., Damián M., de Sá-Otero P. Classification of honeybee pollen using a multiscale texture filtering scheme. Mach. Vis. Appl. 2004, 15:186-193.
4. Chen C., Hendriks E.A., Duin R.P., Reiber J.H., Hiemstra P.S., de Weger L.A., Stoel B.C. Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort. Aerobiologia 2006, 22:275-284.
5. Chica M. Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing. Microsc. Res. Tech. 2012, 75:1475-1485.
6. Das D.K., Ghosh M., Pal M., Maiti A.K., Chakraborty C. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 2013, 45:97-106.
7. Dellanna R., Lazzeri P., Frisanco M., Monti F., Campeggi F.M., Gottardini E., Bersani M. Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning. Anal. Bioanal. Chem. 2009, 394:1443-1452.
8. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2012, John Wiley & Sons, New York.
9. Erdtman G., Sarjeant W.A.S., Praglowski J., Nilsson S., Dunbar A. Handbook of Palynology: Morphology, Taxonomy, Ecology. An Introduction to the Study of Pollen Grains and Spores 1969, Hafner, New York.
10. Field D.J., et al. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 1987, 4:2379-2394.
11. Fisher R.A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 1936, 7:179-188.
12. Gao X., Sattar F., Venkateswarlu R. Multiscale corner detection of gray level images based on log-Gabor wavelet transform. IEEE Trans. Circuits Syst. Video Technol. 2007, 17:868-875.
13. Gross M.H., Koch R. Visualization of multidimensional shape and texture features in laser range data using complex-valued Gabor wavelets. IEEE Trans. Visual. Comput. Graph. 1995, 1:44-59.
14. Haralick R.M., Shanmugam K., Dinstein I.H. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 610-621.
15. Ivleva N., Niessner R., Panne U. Characterization and discrimination of pollen by Raman microscopy. Anal. Bioanal. Chem. 2005, 381:261-267.
16. Kaya Y., Erez M.E., Karabacak O., Kayci L., Fidan M. An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network. Grana 2013, 52:71-77.
17. Kovesi P. Image features from phase congruency. VIDERE: J. Comput. Vis. Res. 1999, 1:1-26.
18. Kumar S., Ong S.H., Ranganath S., Chew F.T. Invariant texture classification for biomedical cell specimens via non-linear polar map filtering. Comput. Vis. Image Underst. 2010, 114:44-53.
19. Langford M., Taylor G., Flenley J. Computerized identification of pollen grains by texture analysis. Rev. Palaeobot. Palynol. 1990, 64:197-203.
20. Li P., Flenley J.R. Pollen texture identification using neural networks. Grana 1999, 38:59-64.
21. Li P., Treloar W., Flenley J., Empson L. Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains. J. Quat. Sci. 2004, 19:755-762.
22. Marcos J.V., Cristóbal G. Texture classification using discrete Tchebichef moments. J. Opt. Soc. Am. A 2013, 30:1580-1591.
23. Mitsumoto K., Yabusaki K., Aoyagi H. Classification of pollen species using autofluorescence image analysis. J. Biosci. Bioeng. 2009, 107:90-94.
24. Mukundan R. Some computational aspects of discrete orthonormal moments. IEEE Trans. Image Process. 2004, 13:1055-1059.
25. Mukundan R., Ong S., Lee P.A. Image analysis by Tchebichef moments. IEEE Trans. Image Process. 2001, 10:1357-1364.
26. Nava R., Escalante-Ramírez B., Cristóbal G. Texture image retrieval based on log-Gabor features. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 2012, 414-421. Springer, Berlin.
27. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:971-987.
28. Otsu N. A threshold selection method from gray-level histograms. Automatica 1975, 11:23-27.
29. Pappas C., Tarantilis P., Harizanis P., Polissiou M. New method for pollen identification by FT-IR spectroscopy. Appl. Spectrosc. 2003, 57:23-27.
30. Punyasena S.W., Tcheng D.K., Wesseln C., Mueller P.G. Classifying black and white spruce pollen using layered machine learning. New Phytol. 2012, 196:937-944.
31. Randen T., Husoy J.H. Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 1999, 21:291-310.
32. Rodriguez-Damian M., Cernadas E., Formella A., Fernández-Delgado M., De Sa-Otero P. Automatic detection and classification of grains of pollen based on shape and texture. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 2006, 36:531-542.
33. Schulte F., Lingott J., Panne U., Kneipp J. Chemical characterization and classification of pollen. Anal. Chem. 2008, 80:9551-9556.
34. Schwartz W.R., de Siqueira F.R., Pedrini H. Evaluation of feature descriptors for texture classification. J. Electron. Imaging 2012, 21:023016-1-023016-17.
35. Soh L.K., Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 1999, 37:780-795.
36. Stillman E., Flenley J.R. The needs and prospects for automation in palynology. Quat. Sci. Rev. 1996, 15:1-5.
37. Teh C.H., Chin R.T. On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Mach. Intell. 1988, 10:496-513.
38. Treloar W., Taylor G., Flenley J. Towards automation of palynology 1: analysis of pollen shape and ornamentation using simple geometric measures, derived from scanning electron microscope images. J. Quat. Sci. 2004, 19:745-754.
39. Tuceryan M., Jain A.K. Texture analysis. The Handbook of Pattern Recognition and Computer Vision 1993, World Scientific, Singapore. C. Chen, L. Pau, P. Wang (Eds.).
40. Zhang Y., Fountain D., Hodgson R., Flenley J., Gunetileke S. Towards automation of palynology 3: pollen pattern recognition using Gabor transforms and digital moments. J. Quat. Sci. 2004, 19:763-768.
41. Zhang Y., Wang R., Hunter P. Airborne pollen texture discrimination using wavelet transforms in combination with cooccurrence matrices. Int. J. Intell. Syst. Technol. Appl. 2005, 1:143-156.