application of an automated microtomy method in …

Institute for Paper, Pulp and Fiber Technology

Professor Horst Cerjak, 19.12.2005

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APPLICATION OF AN AUTOMATED

MICROTOMY METHOD IN THE TRACKING

OF FIBERS IN 3D PAPER STRUCTURES

M. Wiltsche1, W. Bauer1 and M. Donoser2

1 Institute for Paper, Pulp and Fiber Technology2 Institute for Computer Graphics and Vision

Graz University of Technology, Austria



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Tracking of Fibers in 3D Paper Structures COST E54 Opening Seminar, Riga, April 2007

Outline

• Introduction

• Automated Microtomy Concept– Digitization of paper samples

– Image analysis for detection of individual fibers

• Initial Results

• Conclusion and Outlook



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Why 3D Paper Structure Analysis?

• Paper is a heterogeneous material– Fibers and fiber fragments

– Fillers and pigments

– One or more coating layers

• Numerous functional paper properties are influenced

by the spatial arrangementE.g. strength properties, dimensional stability, printability ...

→ 3D analysis of paper structure can provide valuable information

• Capability of accessing individual fibers or pores is

crucial



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Requirements in Digitizing 3D Paper Structure

• True three dimensional representation of structure

• High spatial resolution � > 1 µm

• Sufficient sample size � > some mm²

• Applicability in „day-to-day“ research on commercial

paper samples– Time in the order of hours

– Moderate investment and operating costs



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Currently Applied Digitization Methods

• Non-destructive Methods– Confocal Laser Scanning Microscopy (CLSM)

– X-ray microtomography

• Micro-focus X-ray tube microtomography (low resolution: ≈ 5-10 µm)

• Synchrotron radiation microtomography (high resolution: ≈ 1- 2 µm)

– Optical coherence tomography (OCT)

– Nuclear magnetic resonance (NMR) imaging

– Ultrasonic microscopy

• Destructive Methods– Sheet splitting

– Serial sectioning



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In-situ analysis of

individual elements in paper structures

• Yang et al. [1]

– Operator identified points along the perimeter of each fiber by hand

– State of bonding and fiber properties were evaluated

• Hasuike et al. [2]

– Contour of fiber cross sections was identified manually

– Bonding state and 3D fiber orientation distribution were determined

• Aronsson [3]

– Automated detection and tracking of individual fibers

– Three different approaches, some user-interaction needed

• Sintorn et al. [4]

– Automated segmentation of individual pores

[1] C.-F. Yang, A.R.K. Eusufzai, R. Sankar, R.E. Mark, and R.W. Perkins Jr. Measurements of geometrical parameters of fiber networks. Part 1.

Svensk Papperstidning, 81(13):426–433, 1978.

[2] M. Hasuike, T. Kawasaki, and K. Murakami. Evaluation method of 3-D geometric structure of paper sheet. Journal of Pulp and Paper Science,

18(3):J114–J120, 1992.

[3] M. Aronsson. On 3D Fibre Measurements of Digitized Paper. PhD thesis, Swedish University of Agricultural Sciences, Uppsala (Sweden), 2002.

[4] I.-M. Sintorn, S. Svensson, M. Axelsson, and G. Borgefors. Segmentation of individual pores in 3D paper images. Nordic Pulp and Paper Research

Journal, 20(3):316–319, 2005.



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Outline

• Introduction



• Initial Results




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Automated Microtome

Rotary microtomeMoveable stage

Cut block (embedded sample)Optical microscope

CMOS camera



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Automated Microtome



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Operating Sequence

• Preparation of the microtome

• Setting required parameters for digitization– Region-of-interest (ROI)

– Number of cuts

– Camera and light settings

– Miscellaneous parameters (sample ID, cut thickness, …)

• Start of the automated digitization loop– Software activates the first microtome cut

– Specimen head of microtome stops in upper limit automatically

– Scanning of the block surface

– Microtome cuts next section

→ Result: Set of images comprising 3D paper structure.



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Digital 3D Representation of

Digitized Paper Structure



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Specification Parameters Automated Microtome

• Minimum pixel size 0.16 µm

• Maximum resolution 0.61 µm

• Practical cut thickness 1 µm – 8 µm

• Time duration for one cut 20 s – 80 s

• Example for a large data set– Sample area 1.6 × 2.65 mm²

– Voxel size 0.16 × 5 × 0.16 µm³ (CD × MD × ZD)

– Number of cuts 530

– Images per cut 11

→ Total amount of image data 21.3 GB

→ Time need for digitization 8.9 h



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Base sheet topography under coating

Local coating thickness

Commercial, calendered WFC sample.

Application in 3D Analysis

of Coating Layer Formation



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Outline

• Introduction



• Initial Results




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Concept to Build a

3D Representation of Individual Fibers

• Concept is based on MSER-Tracking [5]

• Two main steps:– Segmentation of individual fibers in a slice image

Maximally Stable Extremal Regions (MSER) are identified automatically

as bright areas with dark boundaries

– Tracking of these detected fiber regions through image sequence

Correspondences between regions in subsequent slice images are built

• Result: Sequence of label images, wherein each

detected fiber is assigned a unique ID

[5] M. Donoser and H. Bischof. Efficient maximally stable extremal region (MSER) tracking. In Proceedings of the IEEE Computer Society

Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 553–560, New York (USA), June 2006.



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Working Principle of MSER-Tracking

Typical input image.

Resulting label image.

Input image including

boundaries of tracked fibers.



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Outline

• Introduction



• Initial Results




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Further Analysis of Label Images

Cross section area, fiber centroid,

morphological data ….

Follow cross section

area through image

stack

Follow fiber centroid through

image stack



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Remarks to Analyzed Sample

• Commercial kraft paper sample– Made on a fourdrinier-machine

• Voxel size: 0.16 × 5 × 0.16 µm³ (CD × MD × ZD)

• Small image stack comprising 20 frames was analyzed



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Trend of Fiber Centroid Coordinates

• Fibers are moving intensively in CD

• Rather stable in ZD

→ Indication for a predominately layered fiber network.



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Video Sequence

of these Results



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Surface Renderings of Tracked Fibers



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Fibre Morphological Data from Label Images

Visible lumen,

uncollapsed fibers

Group A

Lumen not visible,

fully collapsed fibers

Group B.1

Lumen partially visible,

partially collapsed fibers

Group B.2



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Determination of Local Fiber Wall Thickness

→

→Fibers with

visible lumen

Fully collapsed

fibers



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Determination of Degree of Collapse



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Outline

• Introduction



• Initial Results




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Conclusions & Outlook

• The presented prototype is capable of digitizing

micro-structure of paper– at high resolution,

– with sufficient sample size and

– at moderate costs.

• Obtained 3D image data enables detailed fiber

network analysis

• Future challenge: increase the percentage of

detected fibers– Enhancement of image quality

– Further development of the tracking algorithm



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Acknowledgements

The authors gratefully acknowledge financial support of:– Austrian Research Promotion Agency Ltd. (FFG)

– Mondi Business Paper Austria

– Mondi Packaging Frantschach

– M-Real Hallein

– Norske Skog Bruck

– Sappi Gratkorn

– SCA Graphic Laakirchen

– UPM Steyrermühl

– Voith Paper



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Thank you

for your attention.

3D STRUCTURE

application of an automated microtomy method in …

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