Factorising large image datasets John Ashburner. Principal Component Analysis Need to reduce dimensions for data mining –Reduced feature set that explains.

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Presentation transcript:

Factorising large image datasets John Ashburner

Principal Component Analysis Need to reduce dimensions for data mining –Reduced feature set that explains as much of the data as possible PCA can be optimised via an EM algorithm –Preserve privacy across sites –Deal with missing data Simple PCA not a good model of brain images

Privacy-preserving decomposition Hospital 1 Hospital 2 F2F2 F2F2 P F1F1 F1F1 Data (F) N W2W2 W2W2 K W1W1 W1W1 Features (W) N P H H Spatial basis functions (H) K

Simple PCA approach Original images (from 581) Reconstructions from 64 components

Generalised PCA Logistic PCA suitable for factorising binary data

Principal Geodesic Analysis For learning shape models

Shape and appearance model Mininise the following objective function w.r.t. W (features),  (average), A (“eigen- appearances”) and H (“eigen-warps”): Shape Appearance Regularisation

Shape and appearance “eigenmodes” First of 50 eigenmodesFirst of 64 eigenmodes

Data (only 2D) Faces (64 out of 490) Grey matter maps (64 of 581)

Full model fit Shape and appearance Shape and appearance (logistic)

Shape model only Warped average faceWarped average GM

Full model fit Shape and appearance Shape and appearance (logistic)

Appearance model only Appearance fit (no warping)

Model samples (1) Samples from face modelSamples from brain model

Model samples (2) Samples from face modelSamples from brain model