Making Protein Localization
Features More Robust
Meel Velliste
Carnegie Mellon University
Introduction
• 2-D features work well on our own image set
• Can they be put to practical use?
• The features need to work on other people’s
images
Images from online journals
Fig. 8. Expression of a GMAP-210 mutant form lacking
the microtubule-binding site. After transfection, cells
were fixed and double labeled for GMAP-210 (a) and the
medial Golgi marker CTR433 (b). Red and green image
pair is shown in c. Arrow in b indicates a nontransfected
cell. Alternatively, cells were stained for GMAP-210 (d,
e) and alpha-tubulin (f, g) and image pairs are shown in
h and i, respectively. In d, f, and h, a transfected cell is
shown. In e, g, and i, a nontransfected cell is presented
for comparison. Bars, 10 µm.
Online Image 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 70 10 0
0
0 10 10 0
0
0
0
0 67 0
0
0
33 0
0 11 28 0 11 0 44 0
0
6
0
0
0
Overall accuracy = 18%
0
0
0
0
0
71
29
Without Texture 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 20 70 0
0 10 0
0
0
0
0
0
0 33 0
0
0
33 33 0
0
6 33 39 0
0 22 0
0
0
0
0
0
Overall accuracy = 34%
0
0
0
0
0
14
86
Other Sensitive Features Removed
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
20 0 40 40 0
0
0
0
0
0 33 0
0
0
67 0
0
0 61 22 0
0 17 0
0
0
0
0
0
0
0
14
0
14
14
57
Overall accuracy = 45% (c.f. 68% for our images)
Conclusions
• System can be used to find Golgi or Tubulin
images
• Some robustness can be achieved by
eliminating features sensitive to imaging
technique
• Need additional robust features to improve
performance
• Need a more systematic approach to selecting
robust features