Image Analysis and Pattern Recognition2001 Recognition of spectra (e.g. for lung cancer detection)...

R.P.W. Duin Image Analysis and Pattern Recognition 1

URV Meeting - 22 September 2004

Image Analysis and Pattern Recognition L.J. van Vliet R.P.W. Duin, P. Paclik

Image Analysis Pattern RecognitionQuantitative Imaging ICT Group

Applied Sciences EE, Math. and CSTUDelft TUDelft

Formerly:Pattern Recognition

Applied Physics

http://ict.ewi.tudelft/~duin


Image Analysis and Pattern Recognition

Segmentation: using foreground / background differences

Detection: using examples, models

Measurement: location, size, texture, color, shape

Recognition, classification: good - bad; type A, B, C ..,

Interprestation using context

Image Analysis:

model based, using filters, spatial relations, digital measurements

Pattern Recognition

learned from examples, representation and generalisation


Image Analysis and Pattern Recognition

Image Analysis:Spatially based, grey value

Pattern Recognition:Feature based, multi-dimensional

time, multi-band (spectral), multi-sensor

filtered versions, object properties


Image Analysis Projects with URV

Measurement in 3D fluorescence microscopy (WBSO 1998-1999)Vlaardingen: Johan Haverkamp, Geert van Kempen

Analysis of micro-structures in microscopical images (IOP 1999-2002)Vlaardingen: Han Blonk, Geert van Kempen, + many others

Characterization of regularity and super-structures in fresh fish (2000)Colworth: Scott Singleton

3D X-ray tomography (2001)Colworth: Scott Singleton

Functional understanding of foods (CREF 2000-2002)Vlaardingen: Geert van KempenColworth: Scott SingletonPort Sunlight: Matthew Reed

MRI Imaging, analysis and modeling of water mobility in low-water foods (BT 2001-2003)John van Duynhoven, Gerard van Dalen, + many others


1983-1989: (4 projects) Fuzzy Sets for Consumer Panels, J.F.A. Quadt, S. de Jong, H.W. Lincklaen Westenberg, and W. Vaessen, D.A. van Meel

1990/91: Neural Networks, J. Kuiper, ......

2001 (BTS) Structural and compositional analysis for improverd design of detergent powders,J. Haverkamp, H. Blonk, J. van Duynhoven, G. van Dalen, E.J.J. van Velzen, R. Kohlus, G. van Kempen,

2002: Structural and compositional analysis techniques for detergent powders, R. Kohlus

2003/07: (STW): Hyperspectral image analysis, H. Tammes, G. van Dalen

Pattern Recognition Projects with URV


Image Analysis Joint Publications with URV

C.L. Luengo Hendriks, G.M.P. van Kempen, L.J. van Vliet, S. Singleton, J.C.G. Blonk, The Use of Mor-phological Sieves to Differentiate Microstructures of Food Products, TR, URV, 2000, 26p

N. van den Brink, L.J. van Vliet, and G.M.P. van Kempen, The application of a local orientation estimator to the characterisation of hair, TR, URV, 2000, 10p

J.P.M. van Duynhoven, G.M.P. van Kempen, R. van Sluis, B. Rieger, P. Weegels, L.J. van Vliet, and K. Nicolay, Quantitative assessment of gas cell development during the proofing of dough by magnetic resonance imaging and image analysis, Cereal Chemistry, vol. 80, no. 4, 2003, 390-395

G.M.P. van Kempen and L.J. van Vliet, Background estimation in non-linear image restoration, Journal of the Optical Society of America A - Optics and Image Science, vol. 17, no. 3, 2000, 425-433.

G.M.P. van Kempen, N. van den Brink, L.J. van Vliet, M. van Ginkel, P.W. Verbeek, and H. Blonk, The application of a local dimensionality estimator to the analysis of 3D microscopic network structures, in: Proc. SCIA1999, Lyngby, 1999, 447-455.

Pattern Recognition Joint Publications with URV

E. Backer, R.P.W. Duin, S. de Jong, H.W. Lincklaen Westenberg, D.A. van Meel, and J.A. Quadt, Fuzzy set theory applied to product classification by a sensory panel., PVD 85 3130, Unilever Research Laboratorium, Vlaardingen, 1985, 1-53.

H.W. Lincklaen Westenberg, S. de Jong, D.A. van Meel, J.F.A. Quadt, E. Backer, and R.P.W. Duin, Fuzzy set theory applied to product classification by sensory panel, Journal of Sensory Studies, vol. 4, 1989, 55-72.

J.H. Weber, J.F.A. Quadt, E. Backer, R.P.W. Duin, S. de Jong, H.W. Lincklaen Westenberg, and W. Vaessen, Selection of attributes for panel classification using fuzzy set theory, LPDV 89 3054, Uni-lever Research Laboratorium, Vlaardingen, 1989, 1-35.

P. Paclik, R.P.W. Duin, G.M.P. van Kempen, and R. Kohlus, Segmentation of multi-spectral images us-ing a combined classifier approach, Science & Technology Report, URV, 2001, 20 pages.

P. Paclik, R.P.W. Duin, and G.M.P. van Kempen, Multi-spectral Image Segmentation Algorithm Com-bining Spatial and Spectral Information, in: I. Austvoll (eds.), Proc. SCIA 2001, Stavanger, Norway, 2001, 230-235.

P. Paclik, R.P.W. Duin, G.M.P. van Kempen, and R. Kohlus, On feature selection with measurement cost and grouped features, in: T. Caelli et al., Structural, Syntactic, and Statistical Pattern Recognition, LNCS-2396, Springer Verlag, Berlin, 2002, 443-451.

P. Paclik, R.P.W. Duin, G.M.P. van Kempen, and R. Kohlus, Supervised Segmentation of Textures in Backscatter Images, in: ICPR16, vol. II, 2002, 490-493.

P. Paclik, R.P.W. Duin, G.M.P. van Kempen, and R. Kohlus, Segmentation of multi-spectral images us-ing the combined classifier approach, Image and Vision Computing Journal, vol. 21, no. 6, 2003, 473-482.

Pore-size distribution in Monoglyceridenetworks by 3D CSLM and image analysis

AA BB CC DD

2 4 8 16 32 64 128 256 512

‘Shortest’ diameter

A B C D

Time evolution of phase separating milk protein and amylopectin mixtures

A quantitative relation between the apparent network structure and protein-amylopectin mixtures is obtained by image analysis

time

prot

ein-

amyl

opec

tinra

tios

Air bubbles in dairy products

True 3D characterization of dairy products using 3D micro CT and image analysis yields the gas/volume fraction and the gas cell size distribution which determine important product properties.

Structure of Rice by micro-CT

Preprocessing of rice kernels changes their internal structure.

Re-hydration of rice in MRI

MRI-imaging and mathematical modeling the re-hydration process in rice based on structural information (obtained by image analysis) is needed to understand the relation between internal structure and water uptake.

time


Renewing the Pattern Recognition System

Sensor Representation GeneralisationA

B

1988 Understanding and applying neural networks

1994 Combining classifiers for better generalisation

1995 Understanding and applying support vector machines

1997 One-class classifiers for ill-defined problems

1998 Dissimilarity representation for a wider applicability

2003 Active learning for higher efficiency


Feature based Pattern Recognition

(area)

(perimeter) x1

x2

Class A

Class B

Objects

Training Set GeneralizationRepresentation

Feature SpaceClassifier

Test Object classified as ’B’

Feature representation → Object reduction → Class overlap → Probabilities


Understanding Neural Networks

Number of features (complexity)

ε

regularization

∞sample size

test error

apparent error

mse

training time

test error

apparent error

Small sample size theory

Neural network observations

mse

Linear System

Threshold

|W|O

System

Training

Neural network weight space

x1 x2

Weights w12jWeights w11j

Weights w2j

Σ w1ij xi

oj

-w10j0

1

Σ w2j oj

f(x,W)

-w200

1

Input units

Hidden units(hidden layer)

Output unit


Combining Classifiers

x1

x2 A

B

xS1(x) = 0

S2(x) = 0

S3(x) = 0

How to combine?

Several Classifiers in Same Feature SpaceStacked CombiningSame Feature Space


−10 −8 −6 −4 −2 0 2 4 6 8−8

−6

−4

−2

0

2

4

6

−10 −8 −6 −4 −2 0 2 4 6 8−5

−4

−3

−2

−1

0

1

2

3

4

Support Vector Machines

Minimize training set to a support set

Based on inner products K = XiTXj

V.N. Vapnik, The nature of statistical learning theory, Springer Verlag, Berlin, 1995.


One-Class Classifiers


Dissimilarity Based Representation

d2

d1

dissimilarity space

d1

d2


Dissimilarity Based Classification

A B

XGiven labeled training set T

Unlabeled object x to be classified dx = ( d1 d2 d3 d4 d5 d6 d7)

DT

d11d12d13d14d15d16d17

d21d22d23d24d25d26d27

d31d32d33d34d35d36d37

d41d42d43d44d45d46d47

d51d52d53d54d55d56d57

d61d62d63d64d65d66d67

d71d72d73d74d75d76d77

=

Define dissimilarity measure dij between raw data of objects i and j

Example: Deformable template matching(Jain & Zongker)


Application Areas

1997 Machine conditioning monitoring

2001 Multi-band image segmentation

2001 Recognition of spectra (e.g. for lung cancer detection)

2003 Hyperspectral image analysis

2004 Detection of lung diseases in CT scans

x

y

s

0 50 100 150 2000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Pixels/Bins

The

Spe

ctra

l Int

ensi

ty

Overlapping Spectral Regions

SR−1 (20−21) SR−2 (65−68) SR−3 (61−76) SR−4 (162−169) SR−5 (51−80) SR−6 (41−89) SR−7 (54−79) SR−8 (91−92) SR−9 (16−19) SR−10 (119−124)

0 50 100 150 2000

0.01

0.02

Wavelength bins

Nor

mal

ized

inte

nsity

Typical autofluorescence spectra, excitation = 365nm

HealthyDisease

−50

510

15

−10

−5

0

5

10−10

−8

−6

−4

−2

0

2

4

6

8

new labeling

local spatial information by convolution

combinationclassifier

initial noisy labeling

binary images

stable result ?not yet

yes

end

kmeans clusteringInitial labeling by

spatialinformation information

spectral featurespace

spectral

Multi−spectral imagesegmentation


Future Challenge: The Connectivity Problem

Spatial connectivity is lost!

x1 x2 x3x1

x2

x3

ReshufflePixels

Feature Space, Rnxn

Training set

pixel_1

pixel_2

n

n

Integration of Image Analysis and Pattern Recognition needed

Image Analysis and Pattern Recognition2001 Recognition of spectra (e.g. for lung cancer detection)...

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