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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 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