MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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Slices prepared in this way can be used to obtain microscope images of the structure and damage to the structure, and 3D reconstruction of the structure (Figs 2.11, 2.14 and 2.15). Fig. 2.14. Image of the structure of potato tuber parencghyma tissue obtained with the Biolar EPI optical microscope, a – macro-scale image, magnification x10, b – micro-scale image obtained with the Plan 10/0.24 objective, [10]. 36
Fig. 2.15. Reconstruction of the true image of a gap in the structure of potato tuber parenchyma tissue obtained with the Biolar EPI optical microscope with Plan 10/0.24 objective, following the performance of the procedure described above. 1 denotes the first slice and 30 the last slice in which the gap in the structure wad observed [9]. 37
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: them to straighten out without dama
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
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
Page 101 and 102:
Fig. 3.16. The process of binarizat
Page 103 and 104:
Image analysis software packages fr
Page 105 and 106:
image is marked with the letter a).
Page 107 and 108:
The effect of operation of these fi
Page 109 and 110:
And another example of a filter: -1
Page 111 and 112:
3.8. Mathematical morphology Morpho
Page 113 and 114:
and the whole operation is repeated
Page 115 and 116:
The simplest problem is presented i
Page 117 and 118:
Fig. 3.29 illustrates the phenomeno
Page 119 and 120:
Fig. 3.31. Model of RGB. Descriptio
Page 121 and 122:
And substitution is an operation th
Page 123 and 124:
increase decrease 255 contrast + 40
Page 125 and 126:
Fig. 3.37. Application of normaliza
Page 127 and 128:
4.1.2. Fixation of tissue structure
Page 129 and 130:
The format most frequently used for
Page 131 and 132:
Fig. 4.4. Histogram of grey levels
Page 133 and 134:
orderline of two or three cells. Th
Page 135 and 136:
Shape can also be described by mean
Page 137 and 138:
Fig. 4.11. Image of the parenchyma
Page 139 and 140:
4.2. Analysis of images obtained wi
Page 141 and 142:
lens. However, the realization of t
Page 143 and 144:
is now extremely easy, as all the w
Page 145 and 146:
Fig. 4.16. Combination of erosion,
Page 147 and 148:
Fig. 4.20. Division of binary image
Page 149 and 150:
Fig. 4.24. Image labelling (AphImgC
Page 151 and 152:
Fig. 4.28. Repeat watershed detecti
Page 153 and 154:
as 60%), then the source of error i
Page 155 and 156:
in natural state maintaining high s
Page 157 and 158:
Therefore, the tasks of the compute
Page 159 and 160:
Fig. 4.36. Result of Watershed oper
Page 161 and 162:
and 5617 after correction. For carr
Page 163 and 164:
Fig. 4.38. Histograms of cell area,
Page 165 and 166:
should be analyzed separately. An e
Page 167 and 168:
) Fig. 4.42. Result of reconstructi
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MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

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