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Statistical Shape Analysis

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  1. Statistical Shape Analysis Research Institute of Computer Science and Technology, Ningbo University Zhu Shaojun

  2. What is a shape? • Shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object.

  3. Landmark • A landmark is a point of correspondence on each object that matches between and within populations. • Anatomical landmarks • Mathematical landmarks • Pseudo-landmarks

  4. Landmark • A mathematical representation of an n-point shape in k dimensions could be to concatenate each dimension into a kn-vector. The vector representation for planar shapes (i.e. k = 2) would then be:

  5. Shape Alignment • To obtain a true shape representation – according to our definition – location, scale and rotational effects need to be filtered out.

  6. The Procrustes Shape Distance • Compute the centroid of each shape. • Re-scale each shape to have equal size. • Align w.r.t. position the two shapes at their centroids. • Align w.r.t. orientation by rotation.

  7. shape size metric • A shape size metric S(x) is any positive real valued function of the shape vector that fulfils the following property (like all other metrics):

  8. shape size metric

  9. rotation • Arrange the size and position aligned x1 and x2 as n*k matrices (in the planar case k = 2). • Calculate the SVD, UDV(T), of x1(T)x2 in order to maximize the correlation between the two sets of landmarks. • The rotation matrix needed to optimally superimpose x1 upon x2 is then VU(T):

  10. Generalized Procrustes Analysis • Choose an initial estimate of the mean shape (e.g. the first shape in the set). • Align all the remaining shapes to the mean shape. • Re-calculate the estimate of the mean from the aligned shapes. • If the estimated mean has changed return to step 2.

  11. Modeling Shape Variation • PCA