MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

More documents

Recommendations

Info

Centre of Excellence for Applied Physics in Sustainable Agriculture AGROPHYSICS ul. Doœwiadczalna 4, 20-290 Lublin, Poland Institute of Agrophysics of the Polish Academy of Sciences ul. Doœwiadczalna 4, 20-290 Lublin, Poland
MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES EDITORS: Krystyna Konstankiewicz Artur Zdunek Lublin 2005
Page 1: 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 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
show all

MICRO-STRUCTURE ANALYSIS OF PLANT TISSUES - Lublin

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?