MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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The thickness of the focal layer is also related to the lens used (its numeric aperture, NA). For example, M. Petran (1995) reports, for the Tandem Scanning Reflected Light Microscope (TSRLM), that for an immersion lens of 40X 0.74 NA the image changes totally with every 5 µm focusing step in the Z axis, while for a 40X 1.3 NA lens – with every 1µm. Nevertheless, the difference in comparison to the image generated by the conventional optical microscope is notable. James B. Powley (1989) enumerates, among others the following advantages of the confocal microscope: 1. Reduction in the loss of image resolution resulting from light diffusion (improved contrast). 2. Increase in effective resolution. 3. Improvement in the signal-to-noise ratio. 4. Possibility of studying thick objects and light diffusing objects. 5. Possibility of theoretically unlimited extent scanning in the X-Y plane. 6. Scanning in the Z axis. 7. Possibility of electronic change of magnification rate. 8. Possibility of quantitative evaluation of the optical properties of the sample tested. An example of the micro-structure of potato tuber tissue, obtained with the TSRLM confocal microscope (described below) is shown in Fig. 2.22. Comparing the image with that in Fig. 2.18, one can observe a distinct improvement in the contrast; the cell walls are visible as bright lines, while the interior of the cells is almost black. The image originates from a very thin layer of the tissue, hence such an optical section may also include cell membranes oriented in parallel or almost-parallel to the scan plane, visible as bright areas in the image. Such an image, due to its high contrast, is suitable for e.g. analysis of the geometric features of the mechanical skeleton of plant tissue. 48
Fig. 2.22. Potato tuber tissue. Image obtained with the TRSLM microscope (described further on in the text). Magnification 20X. Preparation: 1 mm sample sliced with a razor blade and washed with tap water. The advantages of the microscope listed above may be utilized provided adequate technical solutions are employed, especially in the aspect of scanning. Several scanning methods can be used: 1. Mechanical scanning through ordered movements of the sample. In Minsky’s first design, the table on the sample was placed was induced to vibrate with the help of electromagnets. As has been mentioned above, this required extraordinary accuracy of the mechanical system. Therefore, in practice such system worked correctly only with low scanning speeds (Petran et al. 1995). Additionally, if the scan was to be non-linear, the computer had to linearize the image, most frequently by saving the image and replaying it later. The design in which the scanning process was realized through movements of the object scanned might not be suitable for the examination of objects changing in time, as the time needed for the scan to be completed could be as long as 100 seconds (Entwistle 2000). 2. Optical scanning through controlled movement of light beam over the object under observation. The scanning is effected through the movement of mirrors. The time of image acquisition is shorter than in the mechanical scan, at the level of 0.5-10 sec per image (Entwistle 2000). 3. Scanning with the help of the Nipkov’s disc. This method, due to the speed of scaning (several to several dozen frames per second) will be described in greater detail (Pawle 1989, Petran et al. 1995, Wilson 1990). 49
Page 1 and 2: MICRO-STRUCTURE ANALYSIS OF PLANT T
Page 3 and 4: MICRO-STRUCTURE ANALYSIS OF PLANT T
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 and 44: Fig. 2.17. Images of the structure
Page 45 and 46: 2.2. Confocal Microscopy Artur Zdun
Page 47 and 48: Fig. 2.19. Potato tuber tissue. The
Page 49: Fig. 2.21. Confocal Microscope oper
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
Page 95 and 96: Fig. 3.11. Counting particles visib
Page 97 and 98: Let us analyse the distribution of
Page 99 and 100: 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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MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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