Biology Reference
In-Depth Information
Chapter 5
Image Analysis: Basic Procedures for Description
of Plant Structures
Jana Albrechtová , Zuzana Kubínová , Aleš Soukup , and Jiří Janáček
Abstract
This chapter gives examples of basic procedures of quantifi cation of plant structures with the use of image
analysis, which are commonly employed to describe differences among experimental treatments or pheno-
types of plant material. Tasks are demonstrated with the use of ImageJ, a widely used public domain Java
image processing program. Principles of sampling design based on systematic uniform random sampling
for quantitative studies of anatomical parameters are given to obtain their unbiased estimations and simpli-
fi ed “rules of thumb” are presented. The basic procedures mentioned in the text are (1) sampling, (2) cali-
bration, (3) manual length measurement, (4) leaf surface area measurement, (5) estimation of particle
density demonstrated on an example of stomatal density, and (6) analysis of epidermal cell shape.
Key words
Counting frame, Experiment design, Image analysis, Image calibration, Length estima-
tion, Number estimation, Stereology, Systematic uniform random sampling, Unbiased estimation
1
Introduction
Image analysis has become a powerful tool for quantifi cation of
plant structures—macroscopic or microscopic ones. The quality of
captured digital signal (microscopic image) is essential for further
processing and quantitative analysis. Image spatial resolution is
number of picture elements (pixels, pxl in 2D) per length or area
unit, which determines distinction of structures within the image.
Resolution in color or gray scale (bit depth) is another important
parameter. Standard 8-bit images allow for 256 (2
8
) levels while
16-bit images provide more detailed scale of 65536 levels to choose
from. Colors of the image are most commonly encoded by three
principal color channels—R (red), G (green), and B (blue) of the
additive color mixing model (RGB) or HSB (or HSI or HSL)
model, which uses hue, saturation, and brightness to specify colors.
During image processing it is thus possible to work with individual
channels of RGB or HSB containing different information.
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