X-FASTUser group Meeting

Lotus 11/10/2017

Interne kwaliteit van poreuze

levensmiddelen: X-

stralentomografie toegepast op

product en proces

Pieter Verboven, Tim Van De Looverbosch, Zi Wang,

Hafizur Bhuiyan, Mattias van Dael, Bart Nicolaï

X-FAST doelstellingen

• Product- en procesontwikkeling:

o ontwikkelen van een testfaciliteit voor microstructuuranalyse met X-

stralentomografie

o onderzoeken van de relatie tussen structuur en sensorische of functionele

eigenschappen

• Online niet-destructieve kwaliteitscontrole:

o online meetmethode van de interne samenstelling en dimensies

o detectie van defecten en vreemde voorwerpen

Praktische aanpak

• Is de microstructuur, het defect of het vreemd voorwerp meetbaar met X-

stralen?

• Is dit accurater/sneller/gemakkelijker/betrouwbaarder dan met een andere

methode?

• Zijn er structurele verschillen op basis van een verschillende samenstelling of

bereidingswijze?

• Zijn deze structuurverschillen bepalend voor de beoogde

producteigenschappen?

• Kan dit op een kostefficiënte manier worden geïmplementeerd in de

productontwikkeling of kwaliteitscontrole?

X-FAST

• Werkplan

3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48

WP1. Microstructuur

1.1 Cases

1.2 X-stralentomografie

1.3 Analyse

1.4 Adviezen

Mijlpalen M1.1 M1.2 M1.3

WP2. Online inspectie

2.1 Cases

2.2 X-stralen inspectie

2.3 Analyse

2.4 Adviezen

Mijlpalen M2.1 M2.2 M2.3

Overview commercial

X-ray systems

X-FAST project 7

Evaluated companies (so far)

Overview

X-FAST project 8

• Similar systems offered by many companies

• Best solution depends on

o Characteristics of the production system

o Customer specifications or consumer requirements

• However, some systems stand out

• We will review 1 pipeline and 2 conveyor belt systems

General conclusions

X-FAST project 9

• Heuft eXaminer XT

o Glass, metal, stone, ceramic, certain plastic…

o Pulsed X-ray beams

• 2D detectors

• Reduce motion blur

• Reduce radiation exposure

o Dual X-ray beam

Pipeline systems

Information

from 2

perspectives

10

1. Eagle Product Inspection RMI3 Series

o Raw beef, pork, chicken, lamb…

o Bulk / open crates / cartons

o Dual-energy X-ray Absorptiometry (DEXA)

Conveyor belt systems

X-rays

X-FAST project 11

• X-ray absorption by elements depend on

o Atomic number

o Energy of the X-rays

• DEXA measures X-rays at high & low energy spectra

• Express as ratio

• Detect differences in chemical composition

Dual-Energy X-Ray Analysis

Single energy Dual energy

Differences

in densities

Differences

in chemical

composition

Organic: C, H, O

Inorganic: Fe, Si, PbPotato wedges

X-FAST project 12

Dual-Energy X-Ray Analysis

• Advantages

• Detect thin contaminants

• Detect low/high-density contaminants in low/high-density

products

• Products with high variations in density

• Fat analysis of meat

• Disadvantages

• Not suited for pipeline applications

• Not good for detecting small high-density contaminants (e.g.

metal)

• Less suited for high-speed production (max 60 m/min)

Single energy Dual energy

stones

Salad bag

X-FAST project 13

2. Wipotec-OCS GmbH SC-V

o Combines X-ray and optical inspection

Conveyor belt systems

Faculty, department, unit ... 14

2. Wipotec-OCS GmbH SC-V

o X-ray detector: HD-TDI camera (160 μm resolution)

• Time delay integration (TDI)

• Reduce Motion blur

• Increases signal-to-noise ratio

• Speed can be improved

Conveyor belt system

Single line scanner TDI line scanner

Chocolates

Industrial (low) resolution CT

Product Amount Resolution (µm)

Gudrun chocolate 5 types 976

Filling materials 5 box 511

Contaminants 36 types 644

Belgian 4 box 634

Delicious 4 box 634

Heart 4 box 671

Cupido 2 Box 580

High resolution CT

• 5 types Gudrun

• BCG seashells

o 3 repetitions

o 90 kV & 60 kV X-ray energy levels

o ~ 60 µm resolution

Chocolates Gudrun

3: karamel

4: praliné 5: crumbs praliné

1: truffel 2: genache

Chocolates Gudrun

Chocolates: low resolution

1 2 3 4 5

Chocolates: high resolution31

5

2

4

Contaminants

Contaminants

3D view

Cross section showing

differences in contrast

Low resolution scans

Image analysis

• Goal:

o To verify correspondence of CT images of individual choclates

o To develop a quantitative description of the average chocolate CT image

• Approach

o CT imaging

• 977 × 977 × 300 µm voxels

• ~50 chocolates

• 5 filling types

o Image processing

• Avizo 9.4

Step 1: image registration

• Individual chocolate images are not aligned

• Align images using registration protocol based on image histograms/image

outline: rotate & translate

before after

Step 2: resample

• Resample registered image to the same voxel space

o Low pass filter interpolation (Lanczos)

Step 3: Calculate arithmetics

Substract images

• Plot of difference in grayscale

Average image

• Plot of grayscale (0 – 127)

-50 500

Simulate projections

• Inverse of integral of image intensity in particular directions

• For parallel X-ray beam: direct from CT image

• For fan beams (in practice):

o ASTRA toolbox (Vision Lab, UA)

Application

• Calculate average model (and deviation) of reference chocolate image

• Use for improved detection of defects on projections

• Initial trial

o 16 chocolates (filling 1)

• 12 uniform filling

• 4 fillings with air pockets/cracks

o Registration – resampling

o Develop model: average & standard deviation image (now from 12)

o Check difference images

o Detect defects using

Average Standard deviation

0 124

Difference images

-50 500

Good chocalates

Chocalates with defects

Detection of defects in filling

• Use average chocolate model to calculate ideal projection

horizontal vertical

• Mask filling on chocolate model (threshold)

o Remove uncertainty of shell contrast

horizontal vertical

• Chocolates with defects

o Cross sections of CT images

• Chocolates with defects: cracks & circular cavities

230 mm³ volume

1.1 mm thickness (0.4)

3.2 mm diameter (1.0)

130 mm³ volume

0.8 mm thickness (0.3)

2.8 mm diameter (1.0)

8 mm³ volume

2.4 mm diameter (0.8)

2 mm³ volume

1.6 mm diameter (0.5)

• Mask filling on projections of chocolates with defects (horizontal)

Chocolate model

Chocolate model

• Mask filling on projections of chocolates with defects (vertical)

• Difference between chocolate images & model (horizontal)

o Each optimized for contrast

• Auto-threshold (based on entropy, first attempt)

• Difference between chocolate images & model (vertical)

o Each optimized for contrast

• Auto-threshold (based on entropy, first attempt)

• Auto-threshold without model

• Difference between chocolate images & model: chocolates without defects

• Auto-threshold (based on entropy, first attempt)

Conclusions

• Large dataset collected

• Processing underway

• First trial with images of ~1 mm pixel size

o Detection in projection imaging mode using chocolate model

• Feasible for spherical defects of 2 mm diameter and > 80% deviation in grey scale

• Feasible for thin defects of less 1 mm thickness and sufficient length > 30% deviation in

grey scale

• Not feasible for spherical defects < 2 mm diameter and < 50% deviation in grey scale

o Detection in 3D mode possible for all above

o Improvements expected

• Better resolution

• Better geometrical control of objects during scan

• Robust detection algorihtms

• Next

o Testing with range of contaminents

o Other fillings

o Other chocolate shapes

CT imaging & analysis of pork

loin

Imaging of pork loins

• Medical CT

• Resolution

o 700 × 700 × 300 µm

• 3 Carcasses

Tissues can be distinguished

• Bone/marrow

• Muscle

• Fat/connective tissue

Segmentation

• Manual greyscale thresholds rough but simple

• Requires clean-up operations (filling, islands removal, smoothing)

Segmented tissues

Spine structure

Characterisation of spine

• Skeletonization

o Thinning of bone structure to single voxel lines

o Positioned central with distance to structure boundaries

o Leads to a collection of nodes and intersections

o Nodes

• Positions in 3D space that are

• End points

• Junctions of two or more intersections

o Can be used to calculate direction in 3D

Skeleton of spine of pork loin

Different carcasses

o XZ

Different carcasses

o YZ

Use of projection images?

57

A. On radiograph images (based on Mysling et al., 2013)

o Preparation (prior knowledge)

1. Training data: radiographs

2. Trained pixel classifier: background, inter-vertebral space, intra-vertebral

space, vertebral boundary

3. Low resolution shape model of spine

4. High resolution shape model of individual vertebrae

Methods for spine and vertebrae localization in pork

loin

First 3 components

+3 SD

Mean shape

-3 SD

(2.) (3.)

(1.)

A. On radiograph images (based on Mysling et al., 2013) (continued)

o Steps:

1. Radiograph

2. Pixel classifier on radiograph

3. Fit low resolution shape model of spine (iteratively)

4. Fit high resolution shape model of individual vertebrae

Coordinates of contour for every vertebrae

o Disadvantage: no depth information

Methods for spine and vertebrae localization in pork

loin

B. On CT-images (based on Klinder et al., 2009)

o Preparation (prior knowledge)

1. Training images: CT-scans

2. Vertebrae shape models (using phantoms)

3. Manually position models in training images

+ automatic adaptation

4. Results in mean shape for each vertebrae

Methods for spine and vertebrae localization in pork

loin

B. On CT-images (based on Klinder et al., 2009) (continued)

Coordinates of mesh for every vertebrae in 3D

Methods for spine and vertebrae localization in pork

loin

• Mysling, P., Petersen, K., Nielsen, M., & Lillholm, M. (2013). A unifying

framework for automatic and semi-automatic segmentation of vertebrae

from radiographs using sample-driven active shape models. Machine Vision

and Applications, 24(7), 1421–1434. https://doi.org/10.1007/s00138-012-

0460-2

• Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C.

(2009). Automated model-based vertebra detection, identification, and

segmentation in CT images. Medical Image Analysis, 13(3), 471–482.

https://doi.org/10.1016/j.media.2009.02.004

References

Breakfast cake (gingerbread)

Breakfast cakes

• Prone to development of large elongated

cavities

• May cause breaking when slicing

• Are today removed by visual external

inspection of cake loafs

• Cakes have high porosity

• With wide range of pore sizes

• Can image processing of CT images

separate cavities from pores?

Cake imaging

• High resolution CT

o Segments of loaf

o 105 µm

Cake imaging

• High resolution CT

o Segments of loaf

o 105 µm

• Low resolution CT

o 977 × 977 × 300 µm

o Complete loafs

• 7 rejected

• 7 packed

Segmentation

• High Res images

Low grey value threshold Erosion & Dilation

Segmentation

• High Res images

Labelling Analysis filter for volume & shape

Segmentation

• High Res images

Segmentation

• Applied to ‘good’ sample

Labelling Analysis filter

• Volume number distribution of labeled pores

>

Segmentation

• Low resolution scans: rejected loafs

Segmentation

• Low resolution scans: packaged loafs

Conclusions

• Detection of cavities in breakfast cake is feasible

• Low resolution can be used

• Detects also cavities inside the loaf

• Next: investigate simplified imaging algorithm on projections

good rejected