FAST INLINE INSPECTION OF APPLES BY A NEURAL … · fast inline inspection of apples by a neural...
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FAST INLINE INSPECTION OF APPLES BY A
NEURAL NETWORK BASED FILTERED BACKPROJECTION METHOD
Eline Janssens
Eline Janssens, Luis F. Alves Pereira, Jan de Beenhouwer, Mattias
Van Dael, Pieter Verboven, Bart Nicolaï, Jan Sijbers
iMinds – Vision Lab, University of Antwerp (CDE), Antwerp, BelgiumCentro de Informática, Universidade Federal de Pernambuco, Recife, BrasilBIOSYST-MeBios, KU Leuven, Heverlee, Belgium
11/02/2016
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Contents
� X-ray inline inspection
� NN-hFBP
� Results
� Conclusions
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X-ray Inline Inspection
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- New tomographic reconstruction and analysis methods
- Inline inspection of food
Problem
� Inspection of fruit:
Requirements: Good reconstructions
Inexpensive
Fast
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Outside Inside
Image from: Grant J., Mitcham B., Biasi B. and Chinchiolo S., Late harvest, high CO2 storage increase internal browning of Fuji apples,
California Agriculture 50 (3):26-29, 1996
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Inline Inspection
Inspection lines in industry
source
Radiographic inspection
No 3D information
Relative Inexpensive
Fast
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Inline Inspection
source
Tomographic inspection
3D information
Relative Inexpensive
Slower
Artefacts
Reconstruction
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Inline Inspection
Proposed inspectionmethod
3D information
Relative Inexpensive
Rotation
Reconstructionsource
Challenges:
� Fast Inspection
� Good Quality
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E.g.: FBP
Very Fast Reconstruction
Slow acquisition
Lange number of equiangular projections
16 vs 128
projections
Classic Reconstruction algorithms
E.g.: SIRT
Slow reconstruction
Fast acquisition
Small number of projections
Incorporate Prior Knowledge
Analytical Reconstruction Iterative Reconstruction
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16 vs 128
projections
� Fast inspection � Good quality
� Fast inspection
� Good quality
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Inline scanning geometry9
source source
Flatpanel DetectorVirtual Moving
Detector
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Neural Network Hilbert transform based
Filtered Back Projection
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NN-FBP11
Contribution
2013: Neural Network FBP
Pelt D.M., Sijbers J., Batenburg K.J.,2013a, Fast Tomographic Reconstruction from Highly Limited Data Using Artificial Neural Networks, Proceedings of 1st International Conference on Tomography of Materials and Structures (ICTMS 1), Ghent, Belgium, 109-112.
Parallel beam
Circular scanning geometry
Fan beam: hFBP
Inline scanning geometry
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NN-FBP12
Output of a Neural Network: n�,�,�,�� � σ�� ��σ� �� ∙ � � b� � b������
���INPUT
OUTPUT
σ = activation function
b = bias
FILTER
WEIGHTz1
z2
z3
zN
w0
q1
q0
q2
q3
w1
w2
w3⋮
Filters and weights are trained by the neural network
Network trained to reconstruct 1 pixel
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n�,�,�,�� � σ�� ��σ� �� ∙ � � b� � b������
���
NN-FBP13
Output of a Neural Network:
FBP� x, y h�τ&�P'(�xcos θ . /sin�θ� � τ&�12∈4'(∈5
q1
q0
q2
q3
w0
w1
w2
w3
z1
z2
z3
zN
⋮
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NN-FBP14
n�,�,�,�� � σ�� ��σ� �� ∙ � � b� � b������
���
q1
q0
q2
q3
FBPw0
FBPw1
FBPw2
FBPw3
Output of a Neural Network:
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Fan-beam15
Option 1:
q1
q0
q2
q3
FBPw0
FBPw1
FBPw2
FBPw3
Convert fan beam to parallel beam
Fan Beam
Parallel Beam
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Fan-beam16
Option 2: Work directly on fan-beam data
q1
q0
q2
q3
hFBPw0
hFBPw1
hFBPw2
hFBPw3
Hilbert transform based
FBP
You J., Zeng GL., Hilbert transform based FBP algorithm for fan-beam CT full and partial scans, IEEE Trans Med Imaging, 26(2), 190-9, February 2007
Fast
Good reconstructions
Directly on Fan-beam data
Filter x Input
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Results
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Specifications
� Jonagored apples with defects
� Reconstruction 256 x 256
� Simulate inline projections from available circular projections
� Moving detector 287 pixels
� Data from BIOSYST-MeBios, KU Leuven, Belgium
Data Specifications
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� Training: 100 images from 9 apples
� Validation: 10 images from 9 different apples
� Test: 50 images from 5 apples
� 100.000 random pixels for training and 10.000 for
validation
� 4 Hidden Nodes
Network Specifications
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Non-Equiangular Projections19
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Reconstructions20
16
projections
256 projections
64
projections
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Error images21
16 projections
64 projections
FBP
FBP
SIRT
SIRT
NN-hFBP
NN-hFBP
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Comparison Projections NN-hFBP
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NN-hFBP
RMSE RMSE mask MAD FSIM
#proj EA N-EA EA N-EA EA N-EA EA N-EA
8 16.60 16.66 22.64 22.77 117.15 115.18 0.85 0.85
16 10.65 10.72 14.74 14.85 88.80 87.87 0.88 0.89
32 6.11 6.22 8.54 8.69 60.27 61.08 0.93 0.93
64 4.97 4.99 6.85 6.88 47.28 46.95 0.94 0.94
128 2.96 2.97 3.85 3.85 14.05 14.38 0.97 0.97
256 2.41 2.41 2.93 2.92 3.44 3.72 0.98 0.98
512 2.02 2.11 2.23 2.34 1.56 3.70 0.99 0.99
First Indication: Similar quality achieved for both equiangular and non-equiangular projections
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Reconstruction time23
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Conclusions
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Conclusions
� Conveyor belt tomography with a fixed source-detector
system and an additional object rotation is feasible.
� NN-hFBP outperforms both FBP and SIRT in terms of
image quality.
� The reconstruction time of NN-hFBP is an order of magnitude smaller compared to SIRT.
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Agency for Innovation by Science and Technology in Flanders (IWT SBO
TomFood)
Thank you for your attention
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