Download Classification of boar sperm head images using Learning Vector

Classification of boar sperm head images
using Learning Vector Quantization
Lidia Sánchez
Michael Biehl, Piter Pasma,
Marten Pijl, Nicolai Petkov
University of León / Spain
Rijksuniversiteit Groningen/ NL
Electrical and Electronical Engineering
Mathematics and Computing Science
http://www.cs.rug.nl/~biehl
[email protected]
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
ESANN 2006, Classification of boar sperm head images using LVQ
microscopic images of boar sperms
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
ESANN 2006, Classification of boar sperm head images using LVQ
1360 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
ESANN 2006, Classification of boar sperm head images using LVQ
Learning Vector Quantization (LVQ)
example: basic scheme LVQ1 [Kohonen]
• initialize prototype vectors
for different classes

classification:
• present a single example

assignment
ofclosest
a vector

• identify the
prototype,
toi.e
thethe
class
of the closest
so-called
winner
prototype w
• move the winner
- closer towards the data (same class)
aim:
generalization
ability(different class)
- away
from the data




classification of novel data
after learning from examples
ESANN 2006, Classification of boar sperm head images using LVQ
Learning algorithms
LVQ1
Euclidean distance between data ξ prototype w:
d (ξ, w) 
N
2


ξ
w
Σ i i
i 1
given ξ, update only the winner:
w* (t  1)  w* (t )  η(t)  ξ - w* (t) 
prototype initialization:
decreasing learning rate :
(sign acc. to class membership)
class-conditional means + random displacement
(∼70% correct classification)
for t  t o
ηo
η(t)  
ηo 1  c(t - t o )  for t  t o .
ESANN 2006, Classification of boar sperm head images using LVQ
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.
- with respect to test data
average outcome over
90% of all data
10% of all data
10 realizations
ESANN 2006, Classification of boar sperm head images using LVQ
ten-fold cross-validation:
comparison of different LVQ systems (# of prototypes)
performance on training data
correct
%
performance w.r.t. test data
correct
%
… improves with increasing
number of (non-normal)
prototypes
… depends only weakly on the
considered number of
prototypes
ESANN 2006, Classification of boar sperm head images using LVQ
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)
perform gradient descent steps with respect
to an instantaneous cost function f(z)
with
w L (t  1)  w L (t )  η(t)  w L f( z )
z
d (ξ, w J ) - d (ξ, w K )
d (ξ, w J )  d (ξ, w K )
(here : f(z)  z)
ESANN 2006, Classification of boar sperm head images using LVQ
Generalized Relevance LVQ (GRLVQ)
[B. Hammer, T. Villmann, Neural Networks 15: 1059-1068]
GLVQ with modified distance measure
vector of relevances,
normalization
d λ (ξ, w) 
N
Σ
i 1
λ i2 ξ i - w i 
2
2
λ
 i 1
i
- re-define cost function f(z) in terms of dλ:
z
d λ (ξ, w J ) - d λ (ξ, w K )
d λ (ξ, w J )  d λ (ξ, w K )
- perform gradient steps w.r.t. prototypes wJ , wK and vector λ
GRLVQ
- determines favorable positions of the prototypes
- adapts the corresponding distance measure
ESANN 2006, Classification of boar sperm head images using LVQ
Comparison of performance: estimated test error
normal/non-normal prototypes
alg.
3/3
1/7
LVQ1
81.4 %
(4.0)
81.6 %
(4.5)
GLVQ
75.6 %
(4.1)
76.4 %
(3.8)
GRLVQ
81.5 %
(3.5)
81.7 %
(3.7)
mean
(stand. dev.)
- weak dependence on the number of prototypes
- inferior performance of GLVQ (cost function ↮ classification error)
- recovered when including relevances
ESANN 2006, Classification of boar sperm head images using LVQ
GRLVQ: resulting relevances
- only very few pixels are sufficient for successful classification
test error: (all) 82.75%, (69) 82.75%, (15) 81.87%
normal
(LVQ1 prototypes)
non-normal
ESANN 2006, Classification of boar sperm head images using LVQ
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?
Outlook
- improve LVQ system, algorithms, relevance schemes
- training data, objective classification (staining method)
- classification based on contour information (gradient profile)
ESANN 2006, Classification of boar sperm head images using LVQ
LVQ1 demo
ESANN 2006, Classification of boar sperm head images using LVQ