MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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the strength of the film and the possibility of subsequent microscope observation of the slices. The film with slices is then cut into suitable fragments and sealed in a solution of synthetic resin. The same method can also be used to remove important specimens from damaged slide glass and to place them on new ones for further storage or observations [1, 2, 3, 6, 7]. 2.1.8. Prospects for the future of optical microscopy Optical microscopy is a very important method used in studies on the biology of cells. This is shown by the great number of studies in which optical microscopes are used as the basic tools of research. A significant limitation of optical microscopy is the limit resolution of approximately 200 nm, below which no objects can be observed, such as ribosomes, viruses, or individual protein molecules. There are many unknowns at the level of light observed in optical microscopes, but in spite of this limitation optical microscopy should be accepted as a method necessary in the study of the surrounding micro-world. REFERENCES 1. Gerlach D. 1972. Outline of botanical microscopy (in Polish). PWRiL, Warszawa. 2. Burbianka M., Pliszka A., Jaszczura E., Teisseyre T., Załęska H. 1971. Microbiology of food. Microbiological methods for the study of food products (in Polish). PZWL, Warszawa. 3. Litwin A., 1999. Foundations of microscopy techniques (in Polish). Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków. 4. Meyer – Arendt J. R., 1979. Introduction to Optics (in Polish). PWN, Warszawa. 5. Jay Orear, 1999. Fizyka, WNT. 6. Pluta A., 1999. Foundations of microscopy techniques (in Polish). Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków. 7. Przestalski S., 2001 Physics with elements of agrophysics and biophysics (in Polish), PWN, Warszawa. 8. Skorko M., 1973. Physics (in Polish) PWN Warszawa. 9. Pawlak K., Król A. 1999. Changes of the potato tuber tissue structure resulting from deformation (in Polish). Acta Agrophysica 24, 109-133. 10. Haman J., Konstankiewicz K. 1999. Damage processes in the plant cellular body (in Polish). Acta Agrophysica 24, 67-86. 11. Konstankiewicz K., Czachor H., Gancarz M., Król A., Pawlak K., Zdunek A. 2002. New techniques for cell shape size analysis of potato tuber tissue. 15 th Triennal Conference of the European Association for Potato Research, Hamburg. 12. Francon, M. 1961. Progress in Microscopy. Pergamon Press: London (also Row, Peterson and Co.: Elmsford, NY.) 13. Gray P., 1964. Handbook of Basic Microtechnique. McGraw-Hill: New York. 14. http://www.discoveryofthecell.net/ 42
2.2. Confocal Microscopy Artur Zdunek ∗ 2.2.1. Limitations of optical microscopy The Confocal Microscope is one of the types of optical microscopes. Historically, optical microscopy initiated the rapid development of studies on the structure of biological materials at the level of tissues and cells. The classical optical microscope, however, has a number of limitations, such as: 1. Absolute limit of resolution d, determined by Abbe’s Law, d=1.22 X λ 0 / (NA obj + NA cond ), where: λ 0 is the wavelength in vacuum, NA obj and NA cond are the numerical aperture of the lens and the condenser, respectively. 2. Limited contrast resulting from the natural properties of most biological materials. This limitation is less significant in direct observation, as an experienced observer can usually recognize and describe a given element of the micro-structure. The problem becomes more acute when the objective of the analysis is e.g. quantitative description of specific geometric features of the micro-structure using a computer. Additionally, for a series of images from various areas of the sample, the elements of the structure described should be clearly identified so that they can be effectively isolated from the images with the help of specific computer algorithms. 3. Information from the focal plane is interfered with by information from the out-of-focus areas above and below the focal plane (Fig. 2.18). This shortcoming is related to the one mentioned above. Out-of-focus fragments of the image reduce the contrast and cause problems e.g. in 3D reconstruction of the structure. Computer analysis of images in 2D is also difficult. 4. Complicated methods of sample preparation. Fig. 2.19 presents an example of the structure of potato tuber tissue after complex preparation which included dehydration in a series of alcohols, setting in paraffin, slicing and removal of paraffin. As can be noticed, e.g. cell walls look different in Fig. 2.18 than in Fig. 2.19. They are more arched in shape and are often undulating. Sample preparation procedures, and especially slicing, resulted in breaks or gaps in come of the walls. Therefore, it is not possible to state ∗ Artur Zdunek, PhD Institute of Agrophysics, Polish Academy of Sciences ul. Doświadczalna 4, 20-290 <strong>Lublin</strong> 27, Poland e-mail: azdunek@demeter.ipan.lublin.pl 43
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Page 5 and 6: PREFACE The subject matter of resea
Page 7 and 8: PREFACE ...........................
Page 9 and 10: 1. THE GOAL OF IMAGE ANALYSIS OF PL
Page 11 and 12: within an object, caused e.g. by a
Page 13 and 14: 1.4. Analysis of plant tissue struc
Page 15 and 16: 2. MICROSCOPES AND SAMPLE PREPARATI
Page 17 and 18: focusing mechanism permitting the a
Page 19 and 20: 3 5 9 6 7 8 4 2 1 Fig. 2.4. The str
Page 21 and 22: 2.1.3. Mechanical elements Microsco
Page 23 and 24: 2.1.4. Image generation in optical
Page 25 and 26: 2.1.5. Resolution of the microscope
Page 27 and 28: a) 1 b) Relative refractive index 3
Page 29 and 30: 2.1.7. Microscopy studies Microscop
Page 31 and 32: One should take care, however, that
Page 33 and 34: may be difficult and the objects fi
Page 35 and 36: Object slicing and application of s
Page 37 and 38: them to straighten out without dama
Page 39 and 40: Fig. 2.15. Reconstruction of the tr
Page 41 and 42: Fig. 2.16. Images of the same part
Page 43: Fig. 2.17. Images of the structure
Page 47 and 48: Fig. 2.19. Potato tuber tissue. The
Page 49 and 50: Fig. 2.21. Confocal Microscope oper
Page 51 and 52: Fig. 2.22. Potato tuber tissue. Ima
Page 53 and 54: in spite of the different original
Page 55 and 56: estimated that for most animal tiss
Page 57 and 58: However, because of the broadening
Page 59 and 60: 2.3. Electron Microscopy Andrzej Kr
Page 61 and 62: Due to the high vacuum inside the m
Page 63 and 64: Fig. 2.38 presents an image of aggr
Page 65 and 66: Fig. 2.38. Aggregation of starch gr
Page 67 and 68: a) b) c) d) Fig. 2.40. Schematics o
Page 69 and 70: Fig. 2.42. Schematic of ESEM detect
Page 71 and 72: Table 2.1. TECHNOLOGY LIMITING FACT
Page 73 and 74: 3. DIGITAL IMAGES AND THE POSSIBILI
Page 75 and 76: Fig. 3.1. Image processing and anal
Page 77 and 78: Table 3.1. Comparison of selected f
Page 79 and 80: 300 dpi, which corresponds to the p
Page 81 and 82: If we consider a recording element
Page 83 and 84: Fig. 3.4. Comparison of a circular
Page 85 and 86: Fig. 3.6. Changing depth of digital
Page 87 and 88: Fig. 3.7. Change in the appearance
Page 89 and 90: Fig. 3.8. Comparison of an image in
Page 91 and 92: Fig. 3.10. Determination of the num
Page 93 and 94: Maximum width This measurement can
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Fig. 3.11. Counting particles visib
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Let us analyse the distribution of
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An example of the application of co
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Fig. 3.16. The process of binarizat
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Image analysis software packages fr
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image is marked with the letter a).
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The effect of operation of these fi
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And another example of a filter: -1
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3.8. Mathematical morphology Morpho
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and the whole operation is repeated
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The simplest problem is presented i
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Fig. 3.29 illustrates the phenomeno
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Fig. 3.31. Model of RGB. Descriptio
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And substitution is an operation th
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increase decrease 255 contrast + 40
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Fig. 3.37. Application of normaliza
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4.1.2. Fixation of tissue structure
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The format most frequently used for
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Fig. 4.4. Histogram of grey levels
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orderline of two or three cells. Th
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Shape can also be described by mean
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Fig. 4.11. Image of the parenchyma
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4.2. Analysis of images obtained wi
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lens. However, the realization of t
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is now extremely easy, as all the w
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Fig. 4.16. Combination of erosion,
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Fig. 4.20. Division of binary image
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Fig. 4.24. Image labelling (AphImgC
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Fig. 4.28. Repeat watershed detecti
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as 60%), then the source of error i
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in natural state maintaining high s
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Therefore, the tasks of the compute
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Fig. 4.36. Result of Watershed oper
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and 5617 after correction. For carr
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Fig. 4.38. Histograms of cell area,
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should be analyzed separately. An e
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) Fig. 4.42. Result of reconstructi
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