Typical Image Selection
Jennifer Lin, James Gajnak,
Robert F. Murphy
Cytometry Development Workshop 2000
Background
Fluorescence microscopy is commonly used
for digital image collection
 Current analysis on such images is done by
visual inspection which is not quantitative,
and not feasible for large data sets
 We have been working on the automated and
objective interpretation of fluorescence
images

Previous work

Typical Image Chooser (TypIC) – method for
ranking a set of images in order of typicality
using Haralick texture features to describe
images
TypIC1
 Uses
only 13 texture features to describe
images
 Uses robust estimation of mean and
covariance matrix to eliminate outliers
 Requires a minimum of 35 images
TypIC2

Principal components allow the use of more
features without requiring an extremely large
number of images
•

Collapses feature set into a smaller number of
dimensions
Robust or non-robust estimations of mean
and covariance matrix also reduces the
number of images needed
•
Non robust estimations assumes there are no
outliers
Results
 Compare
 Results
TypIC2 with TypIC1
for mixed sets
 Stability of rankings for sets of decreasing
size
Mixed Sets
 Assembled
a biased test set of images
from five classes: zero time (30), 10 min
(25), 30 min (20), 60 min (15), basal (10)
 Hypothetically images from the largest
(most biased) classes will be ranked as
the most typical
Results – TypIC1
 Correlation
coefficient: -0.52708
TypIC1 with 13 Haralick Features (Robust)
Typicality
1.0
0.8
0.6
0.4
0.2
0.0
0
1
2
3
Protein Class
4
5
6
Results – TypIC2

Correlation coefficients for varying numbers
of principal components (PC):
Num. of PC
Cor. Coeff.
1
-0.37177
2
-0.30753
3
-0.30598
4
-0.25877
5
-0.28973
Results – TypIC2
 Correlation
coefficient: -0.37177
TypIC2 with 1 Principal Component (Robust)
Typicality
1.0
0.8
0.6
0.4
0.2
0.0
0
1
2
3
Protein Class
4
5
6
Results - Mixed Sets
 TypIC2
performs best with only 1
principal component (2 and 3 similar)
 TypIC1 is better at distinguishing
between the protein classes than
TypIC2
Results - Set Size
 Use
ten classes of images
 Rank full set with various methods
 Rank subsets of decreasing size
 Measure correlation between rankings
Results - Set Size