MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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of the relation between the structure and other properties of the medium, and can also be used in modelling the structure of plant tissue. We are grateful to Professor Leszek Wojnar for his unfailing support and understanding of our complex problems of the cellular structure of agricultural plant media and for being the author of the chapter which provides a lucid introduction to the difficult problems of image analysis. All the presented methods of obtaining microscope images – confocal microscopes: TSRLM and CSLM and the optical and electron microscopes – as well as analyses of the images are the results of work of the Group for the Mechanics of Agricultural Materials. Studies in the field of quantitative description of the structure of plant media are continued and up to date information on their results are presented on our website: www.mam.lublin.pl 12
2. <strong>MICRO</strong>SCOPES AND SAMPLE PREPARATION 2.1. Optical Microscope Marek Gancarz ∗ Microscope is a device for the generation of magnified images of objects or their detail parts, invisible to the naked eye. The images can be viewed directly (conventional optical microscope), photographed (microphotography), projected straight onto a screen (projection optical microscope) or converted (TV optical microscope). The first microscope was built by a Dutch optician, van Jansen (Fig. 2.1), around 1595. It was a very simple device which permitted the observation of yeast cells. Fig. 2.1. The first microscope built by van Jansen around 1595. In March, 1625, the word „microscope” was used for the first time – in a letter from one of the scientists to the Italian Prince, Federigo Cesie. ∗ Marek Gancarz, MSc Institute of Agrophysics, Polish Academy of Sciences ul. Doświadczalna 4, 20-290 <strong>Lublin</strong> 27, Poland e-mail: marko@demeter.ipan.lublin.pl 13
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: 1.4. Analysis of plant tissue struc
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 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
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Fig. 2.38. Aggregation of starch gr
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a) b) c) d) Fig. 2.40. Schematics o
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Fig. 2.42. Schematic of ESEM detect
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Table 2.1. TECHNOLOGY LIMITING FACT
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3. DIGITAL IMAGES AND THE POSSIBILI
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Fig. 3.1. Image processing and anal
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Table 3.1. Comparison of selected f
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300 dpi, which corresponds to the p
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If we consider a recording element
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Fig. 3.4. Comparison of a circular
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Fig. 3.6. Changing depth of digital
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Fig. 3.7. Change in the appearance
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Fig. 3.8. Comparison of an image in
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Fig. 3.10. Determination of the num
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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
Page 167 and 168:
) Fig. 4.42. Result of reconstructi
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