Particle picking

Carlos Óscar S. SorzanoVahid Abrishami

Instruct Image Processing Center

Particle picking

• The problem• Preprocessing• Automatic picking

– 3D Model-based picking– 2D Model-based picking– Feature-based picking

• Screening• Consensus picking

The problem

The problem

The problem

Preprocessing

• Downsampling• Fourier filtering• Wavelet filtering• Quantization

Automatic picking: 3D model based

• Correlation peaks:• Cross-correlation• Fourier-correlation• Local-correlation• Normalized-correlation

• Threshold criteria:

Automatic picking: 2D model based

• Correlation peaks:• Cross-correlation• Fourier-correlation• Local-correlation• Normalized-correlation

• Threshold criteria:

Automatic picking: Feature based

91D vector

Automatic picking: Feature based

• Classifier:• SVM• Naive Bayesian• Neural network• LDA

• Cascaded classifiers:• AdaBoost

Manual supervision

Automatic Screening

20D vector

Screening: Mahalanobis distance

Automatic Screening

Automatic Screening

Consensus picking

Conclusions

• Picking families:– 2D/3D Model based: “correlation”+threshold

criterion– Feature based: nD features+classifier

• A posteriori screening:– nD features+distance rank

• Consensus picking• State-of-the-art: 85% precision, 70% recall