Classification of Protein
Localization Patterns in 3-D
Meel Velliste
Carnegie Mellon University
Introduction
• Need a Systematics for Protein Localization
• Need Microscope Automation
• Feature based classification of Localization
Patterns
• Pioneering work done with 2D images
• Now exploring classification of 3D images
Ten Major Classes of Protein
Localization
Features
• Derive Numeric
Features based on:
– Morphology
– Texture
– Moments
feature1 feature2 ... featureN
Image1 0.3489
0.1294
... 1.9012
Image2 0.4985
0.4823
... 1.8390
...
...
ImageM 1.8245
0.8290
... 0.9018
Classification
• Tried:
– Classification Trees
– kNN
– BPNN
• BPNN was the most successful with 84%
correct classification rate
This is a
cyto-skeletal protein
Results of 2-D Classification
True Class
DNA
ER
Giantin
GPP130
LAMP2
Mitoch.
Nucleolin
Actin
TfR
Tubulin
Output of Classifier
DN ER Gia GP LA Mit Nuc Act TfR Tub
0
0
0
0
0
0
1
0
98 1
0 87 2
0
1
5
0
0
1
3
0
0 84 12 1
1
1
0
1
0
0
0 20 72 1
2
3
0
2
0
0
0
5
1 74 0
3
0 15 2
0
8
1
0
0 81 0
0
5
5
0
0
0
1
1
0 98 0
0
0
0
0
0
0
0
1
0 96 1
3
0
2
2
0 18 4
0
2 65 7
0
2
1
0
2
7
0
1
5 84
Overall accuracy = 84%
Motivation for 3-D Classification
• Cells are 3-dimensional objects
• 2-D images take a slice through the cell
• Resultant images are largely dependent on
the z-position of the slice
• Losing a lot of 3-D structural information
The Approach
• Acquire a set of 3-D images for the same 10
classes as used in the 2-D work (have 5
now)
• Calculate equivalent features to what was
used with the 2-D images
• Compare performance
3-D Classification
• Used a subset of the same Morphological
features as used with 2-D patterns:
–
–
–
–
–
–
–
–
Number of Objects
Euler Number
Average Object Size
Standard Deviation of Object sizes
Ratio of the Largest to the Smallest Object Size
Average Distance of Objects from COF
Standard Deviation of Object Distances from COF
Ratio of the Largest to Smallest Object Distance
3-D Classification Results
True Class
DNA
ER
Giantin
GPP130
LAMP2
Mitoch.
Nucleolin
Actin
TfR
Tubulin
Output of Classifier
DN ER Gia GP LA Mit Nuc Act TfR Tub
0
0
0
0
99
0
0
1
97
54
0
2
45
0
0
0
82
0
0
16
2
0
0
4
95
Overall accuracy = 84% (95% with GPP=Giantin)
2-D Results — Same 8 Features
True Class
DNA
ER
Giantin
GPP130
LAMP2
Mitoch.
Nucleolin
Actin
TfR
Tubulin
Output of Classifier
DN ER Gia GP LA Mit Nuc Act TfR Tub
0
0
1
0
99
0
1
1
47
41
7
47
57
1
5
2
89
1
0
3
0
0
0
4
95
Overall accuracy = 84% (95% with GPP=Giantin)
Conclusion
• Further work needed to determine if there is
any advantage to using 3D images over 2D
images
• Need to design new features to take
advantage of extra information in 3D
images
Acknowledgements
• Elizabeth Wu - acquired the 3-D image set
• Michael V. Boland & Robert F. Murphy pioneering work on 2-D images