(Semi-) Automatic Recognition
of Microorganisms in Water
K. Rodenacker, P. Gais2, U. Jütting
and B. A. Hense
GSF-IBB, 2GSF-Patho, Neuherberg,
Germany
Content
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Introduction
Material
Methods
Results
Summary and Discussion
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Introduction

Studies on the
effects of toxicants
on the biocenosis of
aquatic model
ecosystem

Characterization of
plankton commun.
(identification,
counting of phytoplankton)
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Material
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Preparation
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Container of semipermeable LDPE
tubes containing the
test substance
Water column
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Material
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Data gathering
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Sedimentation
Slide preparation
Microscopy
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Material
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Data processing
with QWin, QUIPS

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Scan path and
autofocus
Digitization and
storage
~45 sec/image
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Material
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Some organisms to
be classified
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GYLA
PEUM
HUKU
OOMA
QULA
KICO
CLSA
PLGE
MIPU
CRER
CHAC
CRMA
ZIGA
ACMX
ZIGA
Methods
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Data processing
with IDL
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Image segmentation
Feature extraction
Classification
Re-classification
and training
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Methods

Image
segmentation:
(two-step method)

Rough
segmentation
image threshold
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Fine
segmentation
object threshold
(RATS)
Unbiased count
(forbidden line)
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Methods
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Feature extraction
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Shape
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geometrical
analytical (Fourier,
curvature)
topological (convex
hull, distance map)
algebraic (moments,
PCA)
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Methods

Feature extraction

Extinction
(optical density)


transmitted light
optical density
histogram features
mean (M1), SD (M2),
skewness (M3) etc.
moments (algebraic)
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Histogram of values of transmitted light
ALL
Methods

Classification

Hierarchical tree
classifier based on
stepwise linear
discriminance
analysis
KICO
CLSA
CHAC
Artefacts
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OOMA
CRER
GYLA
PEUM
Methods
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Re-Classification
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Interaction
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Control
Correction
Training
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Preliminary Results

Comparison of
manual and
automatic
procedure
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Summary and Discussion
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Difficulties or failures



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Separate softwares
(Qwin, IDL)
Autofocus automized
microscope
Segmentation
Unlimited number of
organism groups
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Summary and Discussion

Successes


Effective training
system for biologists
AND computer
scientists
Very good
collaboration
between the
different faculties
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Summary and Discussion

Outlook
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Fluorescence
Multiple focal depth
Type specific object
shape features
(dominant feature
points)
Texture object
features
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