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
Features• Derive Numeric
Features based on:– Morphology– Texture– Moments
feature1 feature2 ... featureNImage1 0.3489 0.1294 ... 1.9012Image2 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 acyto-skeletal protein
Results of 2-D ClassificationOutput of Classifier
True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 98 1 0 0 0 0 0 0 1 0ER 0 87 2 0 1 5 0 0 1 3
Giantin 0 0 84 12 1 1 1 0 1 0GPP130 0 0 20 72 1 2 3 0 2 0LAMP2 0 0 5 1 74 0 3 0 15 2Mitoch. 0 8 1 0 0 81 0 0 5 5
Nucleolin 0 0 0 1 1 0 98 0 0 0Actin 0 0 0 0 0 1 0 96 1 3TfR 0 2 2 0 18 4 0 2 65 7
Tubulin 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 ResultsOutput of Classifier
True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 99 0 0 0 0ER
Giantin 0 97 2 0 0GPP130 0 54 45 0 0LAMP2 1 0 0 82 16Mitoch.
NucleolinActin 2 0 0 4 95TfR
Tubulin
Overall accuracy = 84% (95% with GPP=Giantin)
2-D Results — Same 8 FeaturesOutput of Classifier
True Class DN ER Gia GP LA Mit Nuc Act TfR TubDNA 99 0 0 1 0ER
Giantin 0 47 47 5 1GPP130 1 41 57 2 0LAMP2 1 7 1 89 3Mitoch.
NucleolinActin 0 0 0 4 95TfR
Tubulin
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
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