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Classification of boar sperm head images using Learning Vector Quantization. Michael Biehl, Piter Pasma, Marten Pijl, Nicolai Petkov. Lidia S á nchez. Rijksuniversiteit Groningen/ NL Mathematics and Computing Science http://www.cs.rug.nl/~biehl [email protected]

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Classification of boar sperm head images using Learning Vector Quantization

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Classification of boar sperm head images using learning vector quantization

Classification of boar sperm head images

using Learning Vector Quantization

Michael Biehl, Piter Pasma,

Marten Pijl, Nicolai Petkov

Lidia Sánchez

Rijksuniversiteit Groningen/ NL

Mathematics and Computing Science

http://www.cs.rug.nl/~biehl

[email protected]

University of León / Spain

Electrical and Electronical Engineering


Classification of boar sperm head images using learning vector quantization

Motivation

semen fertility assessment:

important problem in human / veterinary medicine

medical diagnosis: - sophisticated techniques, e.g. staining methods

- high accurracy determination of fertility

evaluation of sample quality for animal breeding purposes

- fast and cheap method of inspection

here:

- microscopic images of boar sperm heads (Leon/Spain)

e.g. quality inspection after freezing and storage

- distance-based classification, parameterized by prototypes

- Learning Vector Quantization + Relevance Learning


Classification of boar sperm head images using learning vector quantization

preprocessing:

- isolate and align head images

- normalize with respect to mean grey

level and corresponding variance

- resize and approximate by an

ellipsoidal region of 19x35 pixels

  • replace “missing” pixels (black)

  • by the overall mean grey level

microscopic images of boar sperms


Classification of boar sperm head images using learning vector quantization

  • example images, classified by experts (visual inspection)

normal (650)

non-normal (710)

application of Learning Vector Quantization:

- prototypes determined from example data

- parameterize a distance based classification

- plausible, straightforward to interpret/discuss with experts

- include adaptive metrics in relevance learning


Classification of boar sperm head images using learning vector quantization

• initialize prototype vectors

for different classes

example: basic scheme LVQ1 [Kohonen]

• present a single example

• identify the closest prototype,

i.ethe so-calledwinner

classification:

assignment of a vector 

to the class of the closest

prototype w

• move the winner

-closertowards the data (same class)

-away from the data (different class)

Learning Vector Quantization (LVQ)

aim: generalization ability

classificationof novel data

after learning from examples


Classification of boar sperm head images using learning vector quantization

decreasing learning rate :

Learning algorithms

LVQ1

Euclidean distance between data ξprototype w:

given ξ, update only the winner:

(sign acc. to class membership)

prototype initialization: class-conditional means + random displacement

(∼70% correct classification)


Classification of boar sperm head images using learning vector quantization

example outcome: LVQ1 with 4 prototypes for each class:

normal

non-normal

cross-validation scheme

evaluation of performance

- with respect to the training data, e.g. 90% of all data

- with respect to test data 10% of all data

average outcome over 10 realizations


Classification of boar sperm head images using learning vector quantization

performance w.r.t. test data

performance on training data

correct

correct

%

%

normal

normal

non-normal

non-normal

… improves with increasing

number of (non-normal)

prototypes

… depends only weakly on the

considered number of

prototypes

ten-fold cross-validation:

comparison of different LVQ systems (# of prototypes)


Classification of boar sperm head images using learning vector quantization

perform gradient descent steps with respect

to an instantaneous cost function f(z)

Generalized Learning Vector Quantization (GLVQ)

[A.S. Sato and K. Yamada, NIPS 7, 1995)]

given a single example, update the two winning prototypes :

wJ from the same class as the example (correct winner)

wK from the other class (wrong winner)


Classification of boar sperm head images using learning vector quantization

- re-define cost function f(z) in terms of dλ:

- perform gradient steps w.r.t. prototypes wJ , wK and vectorλ

Generalized Relevance LVQ (GRLVQ)

[B. Hammer, T. Villmann, Neural Networks 15: 1059-1068]

GLVQ with modified distance measure

vector of relevances, normalization

GRLVQ

- determines favorable positions of the prototypes

- adapts the corresponding distance measure


Classification of boar sperm head images using learning vector quantization

81.4 % (4.0)

81.6 % (4.5)

LVQ1

76.4 % (3.8)

75.6 % (4.1)

GLVQ

GRLVQ

81.5 % (3.5)

81.7 % (3.7)

Comparison of performance: estimated test error

normal/non-normal prototypes

alg.3/3 1/7

mean (stand. dev.)

  • - weak dependence on the number of prototypes

  • inferior performance of GLVQ (cost function ↮ classification error)

  • - recovered when including relevances


Classification of boar sperm head images using learning vector quantization

normal

(LVQ1 prototypes)

non-normal

GRLVQ: resulting relevances

  • only very few pixels are sufficient for successful classification

  • test error: (all) 82.75%, (69) 82.75%, (15) 81.87%


Classification of boar sperm head images using learning vector quantization

Outlook

- improve LVQ system, algorithms, relevance schemes

- training data, objective classification (staining method)

- classification based on contour information (gradient profile)

Summary

LVQ provides a transparent, plausible classification

of microscopic boar sperm head images

Performance: LVQ1 ↘GLVQ↗GRLVQ

satisfactory classification error

(ultimate goal: estimation of sample composition)

Relevances:

very few relevant pixels, robust performance

noisy labels / insufficient resolution?


Classification of boar sperm head images using learning vector quantization

LVQ1 demo


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