MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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and image analysis. The next chapter focuses on application of image analysis to images of plant tissues obtained by conventional and confocal microscopes. This part is a summary of research conducted in the Department of Mechanics of Agricultural Materials. We hope that the book will be a source of knowledge about microscopes and image analysis for students and researchers interested in application of these methods to improving and controlling quality of plant food products. Editors Krystyna Konstankiewicz Artur Zdunek Institute of Agrophysics PAS Department of Agricultural Materials 4
PREFACE ...................................................................................................3 1. THE GOAL OF IMAGE ANALYSIS OF PLANT TISSUE MICRO-STRUCTURE - Krystyna Konstankiewicz............................7 1.1. Material studies in modern agriculture ...................................................7 1.2. Structure as a material feature................................................................8 1.3. Observation of plant tissue structure.......................................................9 1.4. Analysis of plant tissue structure images ..............................................11 2. MICROSCOPES AND SAMPLE PREPARATION.....................13 2.1. Optical Microscope - Marek Gancarz..................................................13 2.1.1. Principle of optical microscope.....................................................16 2.1.2. Optical elements ..........................................................................17 2.1.3. Mechanical elements....................................................................19 2.1.4. Image generation in optical microscope ........................................21 2.1.5. Resolution of the microscope ........................................................23 2.1.6. Variants of optical microscopes ....................................................23 2.1.7. Microscopy studies ......................................................................27 2.1.8. Prospects for the future of optical microscopy ...............................42 2.2. Confocal Microscopy - Artur Zdunek..................................................43 2.2.1. Limitations of optical microscopy .................................................43 2.2.2. Confocal microscope principle of operation...................................45 2.2.3. Nipkov’s disc...............................................................................50 2.2.4. Tandem Scanning Reflected Light Microscope-TSRLM...................50 2.2.5. Confocal Scanning Laser Microscope - CSLM...............................54 2.3. Electron Microscopy - Andrzej Król ....................................................57 2.3.1. Operating Principle .....................................................................57 2.3.2. Applications (examples) ...............................................................60 2.3.3. Limitations and development ........................................................64 2.3.4. Conclusion ..................................................................................68 3. DIGITAL IMAGES AND THE POSSIBILITY OF THEIR COMPUTER ANALYSIS - Leszek Wojnar.........................................71 3.1. Introduction - image analysis versus human sense of vision...................71 3.2. Digital images ....................................................................................75 3.3. Computer aided measurements ............................................................87 3.4. Advanced analysis of features detected in digital images .......................92 3.5. Binarization - a step necessary for quantification ..................................98 3.6. Logical operations ............................................................................101 5
Page 1 and 2: MICRO-STRUCTURE ANALYSIS OF PLANT T
Page 3 and 4: MICRO-STRUCTURE ANALYSIS OF PLANT T
Page 5: PREFACE The subject matter of resea
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 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
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However, because of the broadening
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2.3. Electron Microscopy Andrzej Kr
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Due to the high vacuum inside the m
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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
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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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