An evaluation of 2D-image analysis techniques for ...vecnudde/PHD/appendix... · Evaluation of 2D-image analysis techniques 1 An evaluation of 2D-image analysis techniques for measuring

Evaluation of 2D-image analysis techniques

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An evaluation of 2D-image analysis techniques for measuring soil micro-porosity

V. MARCELINOa, V. CNUDDEa, S. VANSTEELANDTb & F. CARÒc

aDepartment of Geology and Soil Science, Ghent University, Krijgslaan 281/S8, B-9000 Ghent,

Belgium, bDepartment of Applied Mathematics and Computer Science, Ghent University, Krijgslaan

281/S9, B-9000 Ghent, Belgium, cDipartimento di Scienze della Terra, Università degli Studi di Pavia,

Strada Ferrata, 1-27100 Pavia, Italia

Correspondance: V. Marcelino, Email: [email protected]

Summary

Manual, semi-automatic and automatic 2D-image analysis procedures were used to

perform and compare soil porosity measurements on three sets of images of the same

fields. The first and second image sets were obtained, using a fluorescence

microscope, on the polished surfaces of soil blocks impregnated with a fluorescent

resin and on the thin sections made from them, respectively. A scanning electron

microscope equipped with a back-scattered electron detector was used to acquire the

third set of images on the thin sections.

In the manual image analysis procedure, image segmentation was based on the best

visual impression and individually carried out for each image with the help of the

public domain UTHSCSA Image Tool software. For the semi-automatic method, the

software µCTanalySIS was used; the images were segmented by double hysteresis,

after interactive selection of the thresholding values for each set of images. Automatic

thresholding of the pore network based on an analysis of the image intensity

histogram was performed using the commercial image analysis software Image-Pro®

Plus.


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Average porosity measurements were compared between image types and image

analysis methods using marginal regression models for continuous outcomes. The

mean area covered by the pores was significantly different depending on the type of

image (P <0.0001) and the method of image analysis (P <0.0001).

These results stress the need for a standardisation of image analysis protocols and

warn for the dangers of comparing soil porosity measurements performed on different

type of images.


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Introduction

The quantification of soil porosity in thin sections was formerly done by manual

methods such as point counting but this has successfully been replaced by less tedious

and more accurate digital image analysis techniques.

The first image analysis systems were available in the early 1970's and have since

then become increasingly accessible to soil scientists. Currently, low-cost software

based image analysis systems make automated analysis of soil pore space very easy

and attractive. Significant contributions to the quantification and characterisation of

soil pores by 2D image analysis procedures are those by Jongerius et al. (1972),

Ismail (1975), Murphy et al. (1977), Bullock and Murphy (1980), Ringrose-Voase &

Bullock (1984), Ringrose-Voase (1987, 1990, 1991), Moran et al. (1989), McBratney

& Moran (1992), Thompson et al. (1992), Terribile & Fitzpatrick (1992), Protz et al.

(1992), Protz & Van den Bygaart (1998).

Two-dimens ional images of the soil pore space are easily obtained when polished

faces of undisturbed soil blocks, previously impregnated with a resin containing a

fluorescent dye, or thin sections cut from them, are illuminated with UV-light or

viewed with a fluorescence microscope (Geyger & Beckman, 1967; Murphy et al.,

1977; Ringrose-Voase, 1984; Bouabib et al., 1992). Pore space is also readily

identifiable on finely polished thin sections viewed with a scanning electron

microscope (SEM) in backscattered mode (Bisdom & Thiel, 1981).

These sample preparation/image acquisition methods have been widely used to obtain

soil pore images at a variety of scales and using only one illumination source.

However, no information exists on the extent to which images obtained with UV


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reflected light in polished blocks, with UV reflected light in thin sections and

backscattered electron images produce comparable porosity measurements.

On the other hand, different techniques exist to analyse 2-D images based on different

theories and algorithms and various software packages, from free public domain to

powerful and expensive ones. Segmentation is one of the most critical steps in the

process of reducing images to information and can be done on the basis of grey-level,

colour separation, textural differences or other meaningful criteria. Since pores in

fluorescent and backscattered electron images have an average intensity different

from the average background intensity, a grey- level threshold operation seems

appropriate to segment the grey-scale image into a two-phase black and white image.

Thresholds in manual and semi-automatic methods are interactively set by the

operator so that the resulting image is visually satisfying. However, since the human

eye is poor in intensity discrimination, the result is not always consistent from one

operator to another or even for a same person over a period of time. Other methods

use the image intensity histogram to automatically adjust threshold settings producing

better reproducible results. In addition, depending on their quality, images are

regularly subjected to some filtering before segmentation and correction procedures

are often applied to segmented images (Bovik et al., 2001; Russ, 2002).

The objective of this study was to compare soil porosity measurements between (1)

three widely used sample preparation/image acquisition procedures and (2) three 2D-

image analysis methods with different levels of manual intervention.


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Materials and Methods

Sample preparation and image acquisition

Two undisturbed soil samples of the same soil were impregnated with a polyester

resin mixed with a fluorescent dye (Uvitex). After complete drying, one face of each

impregnated block was finely polished. On each polished face 10 randomly chosen

fields were marked. They were viewed with UV incident light at a magnification

suited for the measurement of the area occupied by pores (fluorescent zones) with

Ø>20 µm and photographed with a digital camera. Thin sections, 30 µm thick, were

cut from the polished blocks and images of the same fields, at the same magnification

and in UV incident light, were taken. The same thin sections were carbon coated and

backscattered electron images (BSE-images) of the same fields at the same

magnification were obtained with a SEM (JEOL JSM 5900). In total 30 images

(3/field), each with a pixel resolution of 1.7 µm, were taken. Each image represents an

area of 2.25 mm².

Manual thresholding

Image analysis and processing was done using the public domain software UTHSCSA

Image-Tool v.2 on 256 grey level images. No prior smoothing or shape correction

operations were carried out on the images. The pore space is segmented from the rest

of the image by manual thresholding based on the best visual impression. On the

histogram representing the distribution of grey level intensities within the image, the

grey level range that corresponds to the pore space is selected using lower and upper

sliders. During runtime pixels with values within this range are highlighted thus

allowing the user to identify the range that best represents the pore space. Once the


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range is selected the software creates a binary image in which objects (pores) are

white and background black. Both the area covered by all pores detected and the area

covered by pores with Ø>20 µm was automatically determined and recorded. Since

the best threshold must be individually found for each image, this method is rather

time-consuming.

Intra- and inter-observer reproducibility

To assess the inter- and intra-observer reproducibility manual thresholding was

blindly performed on all images with the same software by two independent observers

and blindly repeated by one of the observers after a one-month period. The first set of

results obtained by the more experienced observer was used for method comparisons

in further analyses.

Semi-automatic thresholding

The software “µCTanalySIS” (Cnudde & Jacobs, 2004; Cnudde et al., 2004),

originally developed to analyse 3D images, is based on well-known basic 2D

principles, and has been used for both 2D (Steppe et al., 2004) and 3D image analysis.

Two types of segmentation techniques are possible with this software: region growing

and hysteresis. Segmentation by hysteresis was selected for this study because it has

the advantage of avoiding overflow. This method uses a "strong" and a "weak"

thresholding value, which are interactively selected for each type of images. Strong

thresholding is performed, to filter the noise and to select areas that definitely belong

to the etched boundaries of the pores, even if they do not completely represent these

boundaries. Weak thresholding completely delineates the boundaries of the pores, but

also includes some noise. Hence, the resulting segmented image combines

information of both weak and strong thresholding and represents the pores with their


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corresponding boundaries. No prior smoothing or shape correction operations were

carried out on the images. After segmentation, the pores were labeled by the software

and information about their area was automatically recorded.

Automatic thresholding

The commercial software Image-Pro® Plus used provides classical image processing

and analysis tools and had to be adapted to the specificities of this study (Carò & Di

Giulio, 2004). Depending on the type of image, two different sequences of algorithms

were used to process the source image: the black pores in BSE-images were enhanced

by increasing the pixel intensity of the sample through an iterative mathematical

addition of the same image and the fluorescent images were processed through

automatic histogram equalisation (Media Cybernetics, 1999). The same automatic

iterative thresholding method was then used to identify the pore fraction on the

processed images. The automatic thresholding assumes that the grey level histogram

is the sum of two normal intensity distributions: foreground pixels and background

pixels. This model finds the point where the two distributions intersect, which may

not be exactly where the two distributions separate. For each grey level (t) in the

histogram h(t), the algorithm calculates the variance of the two portions of the

histogram lying on each side of t, v1(t) and v2(t). The model selects the grey level i.e.

the threshold that minimises the sum of the two normalised variances (Media

Cybernetics, 1999). This method is usually referred to as the Otsu method (Otsu,

1979). Once the images are thresholded, the discrete elements of each image are

labelled and spatial measurements recorded.


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Statistical analysis

Both the area covered by all pores detected (“all pores”) and the area covered by

pores with Ø>20 µm (“large pores”) were measured on ten randomly chosen fields

using three methods of sample preparation/image acquisition (image types) and three

image analysis methods, resulting in a total of 18 porosity measurements per field.

Average porosity measurements were compared between image types, image analysis

methods and porosity ranges, using marginal regression models for continuous

outcomes. Models were fitted using generalised estimating equations with

exchangeable working correlation to account for the correlation between porosity

measurements obtained on the same soil samples. The statistical significance in the

models was calculated using Wald tests.

Bland & Altman plots (Bland & Altman, 1986) (see Figure 3) were used to illustrate

measurement bias (estimated by the mean difference in porosity measurement

between methods or images) and lack of agreement (estimated by the variability of

differences in porosity measurement between methods or images). These plot the

difference in measurements in function of their average. In those plots, the solid line

quantifies measurement bias (zero indicating no bias) and the dashed lines delineate

an estimated interval within which 95% of all measurement differences between 2

methods or images can be expected (increasing distance between the dashed lines

being suggestive of increasing lack of agreement).

All analyses were performed with SAS v9.1 (proc mixed) and R v.2.0.0.

The inter-observer and the intra-observer reproducibility were determined by

coefficients of reproducibility (Bland & Altman, 1986). Inter- or intra-observer


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differences that are more extreme than these coefficients can be expected with 5%

chance.

Results and discussion

Not only the area percentage of all pores detected but also the area percentage of

pores with diameter larger than 20 µm was determined on each binary image, because

the fluorescent images at the magnification used do not produce reliable

measurements for small pores. To study them, complementary images would have to

be taken with a stronger objective. On the contrary, in backscattered electron images

(BSE-images) the grey level intensity is proportional to the average atomic number of

a phase and pores, even small ones, are thus clearly detectable.

Figure 1 shows a representative set of images of the same field - the first two are

fluorescent images obtained from the polished block and the thin section and the third

one is an inverted back scattered electron image - with the corresponding grey-level

histograms. The histogram is bimodal for the BSE-images but not for the fluorescent

images. This means that finding an appropriate thresholding value is easier on the

BSE-images than on the fluorescent images. In addition, this figure clearly shows that

the amount of small pores is much larger in the BSE-images than in the other ones.

The mean porosity values for each studied field and their corresponding standard

deviations are summarised in Figure 2. The results of the statistical analysis of the

porosity data, including both “all pores” detected and “large pores” (? >20 µm) only,

are compiled in Tables 1 and 2 and in Figure 3. The results of the overall comparison

of 'all pores' and 'large pores' are presented in Table 3.


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The differences between the two soil samples used are not statistically significant

(P=0.66). The mean area covered by the pores (all/large) is significantly different

depending on the type of image (P<0.0001) and the method of image analysis

(P<0.0001) (Table 1).

Table 1 also shows that the average porosity measurements after automatic

thresholding are significantly and systematically larger than when manual and semi-

automatic thresholding is used (P<0.0001). This trend is very clear in the fluorescent

images. It can be explained by the fact that no image enhancement was performed

prior to the semi-automatic and the manual thresholding, while some image pre-

processing was required by the automatic segmentation method. The histogram

equalisation applied to the fluorescent block and thin section images have caused the

enlargement of some pores leading to overestimation of the pore space. BSE-images

did not require sensible enhancing and were only subjected to a simple mathematical

additive operation of the original image with itself that does no t produce any

enlargement of the pore space.

Direct comparison of results seems thus to be meaningful only if the same image

enhancement and segmentation procedures are used. This is in line with the

observations by Thompson et al. (1992) and stresses the need for a standardisation of

image analysis protocols.

On the other hand, average porosity values determined on BSE-images are

systematically much larger than those obtained on fluorescent images, regardless of

the image analysis procedure used (Table 1) (P<0.0001). These differences are

slightly, although significantly more important if all pores are considered (Table 2).

This suggests that porosity in fluorescent images is generally underestimated.


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In general, if only “large pores” are considered, the differences among image types

are significantly smaller but the differences among image analysis methods are

significantly larger than if “all pores” are included (Table 2). The image

acquisition/sample preparation method affects the quality of the image, mostly the

amount of small pores detected. Hence, when only “large pores” are considered the

differences among images become less important and the differences among the

image analysis methods are enhanced.

As summarised in Table 3 the average differences in porosity between "all pores" and

"large pores" are all statistically significant (P<0.0001). However the differences

between BSE-images of “all pores” and “large pores” are much larger than between

block images and thin section images. This, as mentioned above, is associated with

the larger detail of BSE-images that reflect composition rather than optical properties.

The spreading of the points in the Bland & Altman plots (Bland & Altman, 1986) in

Figure 3 indicates that porosity measurements on thin sections and polished blocks are

more comparable, apart from an average difference, than porosity measurements

obtained from BSE-images and fluorescent images (Figure 3, plot A). Likewise,

porosity results agree best between semi-automatic and manual methods, apart from

an average difference, and less so in comparison with the automatic method (Figure 3,

plot D). This is again explained by the image enhancement used prior to the automatic

segmentation but not before manual and semi-automatic segmentation. Finally, note

that the differences between automatic and the other methods become less important

in images with higher porosity (Figure 3, plot E & F). In contrast, there is some

evidence (Figure 3, plots A & B) that the differences between blocks and the other

images become more important in images with higher porosity. This could suggest


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that extra care has to be taken when comparing porosity measurements of compact

materials.

For the manual thresholding, the coefficient of inter-observer reproducibility was 6.4

and the coefficient of intra-observer reproducibility was 2.0. This indicates that the

porosity results obtained by the same observer are better reproducible than those by

different observers are. Porosity measurements for comparative studies are therefore

best carried out by the same observer. Automatic methods give better reproducible

results, even though they imply less control over the thresholding process.

Conclusion

The quality of the image, determined by the image acquisition/sample preparation

method used, significantly affects average porosity measurements. Fluorescent images

compared with BSE-images clearly underestimate porosity. BSE-images provide

much more detail and are easier to threshold, due to their nature and bimodal grey

level histogram. Porosity measurements carried out on images acquired using

different methods cannot be compared.

On the other hand, since different interventions and methods used to increase image

quality and segment images also significantly affect porosity results, these can only be

compared if the images are subjected to the same treatment.

Moreover, in the particular case of manual thresholding, porosity results obtained by

the same observer are better reproducible than those from different observers.

Porosity measurements for comparative studies are therefore best carried out by the

same observer.


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In summary, the results of this study stress the need for a standardisation of image

analysis methods and protocols used for direct quantification of soil pores and warn

for the dangers of comparing soil porosity measurements carried out on different

types of images.

Acknowledgements

This study is partly supported by the Institute for the Promotion of Innovation by

Science and Technology in Flanders, Belgium through a PhD grant for Veerle

Cnudde. We thank Dr. F. G. Monteiro (ISA, Lisboa) for his help with the

reproducibility data.

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Figure Captions

Figure 1 Mean porosity of each studied field and corresponding standard deviation

(L1 to L10: only large pores are included; A1 to A10: all pores are included).

Figure 2 Set of images of the same field with the corresponding grey-level

histograms.

Figure 3 Bland & Altman plots. Top: porosity difference vs. average porosity of the

different types of images (Sections, Blocks and BSE) analysed by Manual (?), Semi-

automatic (? ) or Automatic (? ) methods. Bottom: porosity difference vs. average

porosity obtained with the different image analysis methods (Manual, Semi-automatic

and Automatic) on Blocks (?), Sections (? ) or BSE (? ) images.


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Table 1 Comparison of methods by image and comparison of images by method

Estimate 95% CI P

Method <0.0001

Manual – Automatic -4.5 -5.6;-3.3 <0.0001

Block -4.3 -5.7;-2.9

Section -6.4 -7.8;-5.0 <0.0001 a

BSE -2.7 -4.3;-1.2

Semi-automatic – Automatic -3.3 -4.2;-2.5 <0.0001

Block -4.2 -5.4;-3.0

Section -5.5 -6.5;-4.6 <0.0001 a

BSE -0.3 -1.5; 0.9

Manual – Semi-automatic -1.1 -1.6;-0.7 <0.0001

Block -0.1 -0.5;0.3

Section -0.8 -1.5;-0.2 <0.0001 a

BSE -2.4 -3.2;-1.7

Image <0.0001

Block – BSE -9.6 -12.0;-7.2 <0.0001

Manual -9.3 -12.1;-6.6

Semi-automatic -11.7 -14.3;-9.0 <0.0001 b

Automatic -7.8 -9.7;-5.8

Section – BSE -6.7 -9.2;-4.3 <0.0001

Manual -7.4 -10.1;-4.7

Semi-automatic -9.0 -11.8;-6.2 <0.0001 b

Automatic -3.7 -5.9;-1.6

Block – Section -2.9 -4.3;-1.5 <0.0001

Manual -2.0 -3.7;-0.2

Semi-automatic -2.7 -4.1;-1.3 0.0253 b

Automatic -4.0 -5.5;-2.6 a Test for heterogeneity in method comparison between images. b Test for heterogeneity in image comparison between methods.


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Table 2 Comparison of methods and images by pore size range

Estimate 95% CI P

Method <0.0001

Manual – Automatic -4.5 -5.6;-3.3 <0.0001

Large pores -4.6 -5.8;-3.5

All pores -4.3 -5.5;-3.1 0.0010 a

Semi-automatic – Automatic -3.3 -4.2;-2.5 <0.0001

Large pores -3.8 -4.8;-2.9

All pores -2.8 -3.6;-2.1 <0.0001 a

Manual – Semi-automatic -1.1 -1.6;-0.7 <0.0001

Large pores -0.8 -1.3;-0.3

All pores -1.5 -2.0;-0.9 0.0068 a

Image <0.0001

Block – BSE -9.6 -12.0;-7.2 <0.0001

Large pores -8.6 -11.1;-6.0

All pores -10.6 -13.1;-8.2 0.0021 b

Section – BSE -6.7 -9.2;-4.3 <0.0001

Large pores -6.0 -8.4;-3.6

All pores -7.4 -10.0;-4.8 0.0202 b

Block – Section -2.9 -4.3;-1.5 <0.0001

Large pores -2.6 -3.9;-1.2

All pores -3.2 -4.7;-1.8 0.0016 b

a Test for heterogeneity in method comparison between pore size ranges b Test for heterogeneity in image comparison between pore size ranges


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Table 3 Overall comparison of "Large pores" and "All pores" by method and by

image

Estimate 95% CI P

Large pores – All pores -1.7 -2.2;-1.3 <0.0001

Manual -1.6 -2.1;-1.1

Semi-automatic -2.3 -2.9;-1.6 <0.0001 a

Automatic -1.3 -1.6;-1.0

Block -0.8 -1.1;-0.5

Section -1.5 -1.8;-1.1 <0.0001 a

BSE -2.9 -4.1;-1.7 a Test for heterogeneity in comparison "Large pores" and "All pores" between methods or images


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Polished block Thin section BSE - image (inverted)


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field

L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10

poro

sity

%

5

10

15

20

25

30

35

40

45


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10 15 20 25 30 35

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15 20 25 30 35

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15 25 35

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10 20 30 40

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15 25 35

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Sections vs. Blocks BSE vs. Blocks BSE vs. Sections

Semi-automatic vs. Manual Automatic vs. Manual Automatic vs. Semi-automatic

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