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Tutorial: Statistics of Object Geometry Stephen Pizer Medical Image Display & Analysis Group University of North Carolina, USA with credit to T. Fletcher, A. Thall, S. Joshi, P. Yushkevich, G. Gerig 10 October 2002 Uses of Statistical Geometric Characterization Medical science: determine geometric ways in which pathological and normal classes differ Diagnostic: determine if particular patient’s geometry is in pathological or normal class Educational: communicate anatomic variability in atlases Priors for segmentation Monte Carlo generation of images Object Representation Objectives Relation to other instances of the shape class Representing the real world Deformation while staying in shape class Discrimination by shape class Locality Relation to Euclidean space/projective Euclidean space Matching image data Geometric aspects Invariants and correspondence Desire: An image space geometric representation that is at multiple levels of scale (locality) at one level of scale is based on the object and at lower levels based on object’s figures at each level recognizes invariances associated with shape provides positional and orientational and metric correspondence across various instances of the shape class Object Representations Atlas voxels with a displacement at each voxel : Dx(x) Set of distinguished points {xi} with a displacement at each Landmarks Boundary points in a mesh With normal b = (x,n) u Loci of medial atoms: m = (x,F,r,q) end atom (x,F,r,q,h) or v t Continuous M-reps: B-splines in (x,y,z,r) [Yushkevich] Building an Object Representation from Atoms a Sampled aij can have inter-atom mesh (active surface) Parametrized a(u,v) e.g., spherical harmonics, where coefficients become representation e.g., quadric or superquadric surfaces some atom components are derivatives of others Object representation: Parametrized Boundaries Parametrized n(u,v) is normalized x/u x/v Coefficients x(u,v) boundaries x(u,v) = Si Spherical Sampled of decompositions ci i f (u,v) harmonics: (u,v) = latitude, longitude point positions are linear in coefficients: Ax=c Object representation: Parametrized Medial Loci Parametrized medial loci m(u,v) = [x,r](u,v) is normalized x/u x/v xr(u,v) = -cos(q)b n(u,v) gradient per distance on x(u,v) b q x n Sampled medial shape representation: Discrete M-rep slabs (bars) x Meshes of medial atoms Objects connected as host, subfigures Multiple such objects, interrelated x r p q - q r n r s o o o o o o o o o o t=-1 b t=0 u v r n r s t=+1 r t o q -q hrb r r p Interpolating Medial Atoms in a Figure Interpolate x, r via B-splines [Yushkevich] Trimming curve via r<0 at outside control points Avoids corner problems of quadmesh Yields continuous boundary Via modified subdivision surface [Thall] Medial sheet Approximate orthogonality at sail ends Interpolated atoms via boundary and distance At ends elongation h needs also to be interpolated Need to use synthetic medial geometry [Damon] Implied boundary End Atoms: (x,F,r,q,h) h=1 Extremely rounded end atom in cross-section h=1.4 Rounded end atom in cross-section h=1/cos(q) Corner atom in cross-section Medial atom with one more parameter: elongation h Sampled medial shape representation: M-rep tube figures Same atoms as for slabs r is radius of tube sails are rotated about b Chain rather than mesh x+rRb,n(-q)b x+ rRb,n(q)b b q x n For correspondence: Object-intrinsic coordinates Geometric coordinates from m-reps Single figure Medial sheet: (u,v) [(u) in 2D] t: medial side : signed r-prop’l dist from implied boundary 3-space: (u,v,t, ) Implied boundary: (u,v,t) u t v x r p x q - q r r p r n r s q -q t=-1 r n r s t=+1 r hrb b t=0 Sampled medial shape representation: Linked m-rep slabs Linked figures Hinge atoms known in figural coordinates (u,v,t) of parent figure Other atoms known in the medial coordinates of their neighbors x+rRb,n(-q)b b q x n o o o o o o o o o o o x+ rRb,n(q)b Figural Coordinates for Object Made From Multiple Attached Figures Blend in hinge regions w=(d1/r1 - d2 /r2 )/T Blended d/r when |w| <1 and u-u0 < T Implicit boundary: (u,w, t) Or blend by subdivision surface w Figural Coordinates for Multiple Objects Inside objects or on boundary Per object In neighbor object’s coordinates Interobject space In neighbor object’s coordinates Far outside boundary: (u[,v],t, ) via distance (scale) related figural convexification ?? ?? Heuristic Medial Correspondence Original (Spline Parameter) Arclength 1 0.8 0.6 0.4 0.2 0.2 Radius 0.4 0.6 0.8 Coordinate Mapping 1 Continuous Analytical Features Can be sampled arbitrarily. Allow functional shape analysis Possible at many scales: medial, bdry, other object Medial Curvature Boundary texture scale Feature-Based Correspondence on Medial Locus by Statistical Registration of Features curvature dr/ds Also works in 3D dr/ds What is Statistical Geometric Characterization Given a population of instances of an object class e.g., Given a geometric representation z of a given instance e.g., subcortical regions of normal males of age 30 a set of positions on the boundary of the object and thus the description zi of the ith instance A statistical characterization of the class is the probability density p(z) which is estimated from the instances zi Benefits of Probabilistically Describing Anatomic Region Geometry Discrimination among geometric classes, Ck Compare probabilities p(z | Ck) Comprehension of asymmetries or distinctions of classes Differences between means Difference between variabilities Segmentation by deformable models Probability of geometry p(z) provides prior Provides object-intrinsic coord’s in which multiscale image probabilities p(I|z) can be described Educational atlas with variability Monte Carlo generation of shapes, of diffeomorphisms, to produce pseudo-patient test images Necessary Analysis Provisions To Achieve Locality & Training Feasibility Multiple scales Allows few random variables per scale At each scale, a level of locality (spatial extent) associated with its random variable Positional correspondence Across instances Between scales Large scale Smaller scale Discussion of Scale Spatial aspects of a geometric feature Its Its position spatial extent Region summarized Level of detail captured Residues from larger scales Distances to neighbors with which it has a statistical relationship Markov random field Cf PDM, spherical harmonics, dense Euclidean positions, landmarks, m-reps Large scale Smaller scale Scale Situations in Various Statistical Geometric Analysis Approaches Multidetail feature, Detail residues each level of detail, E.g., spher. harm. E.g., boundary pt. E.g., object hierarchy Level of Detail Global coef for Fine Coarse Location Location Location Principles of Object-Intrinsic Coordinates at a Scale Level Coordinates at one scale must relate to parent coordinates at next larger scale Coordinates at one scale must be writable in neighbor’s coordinate system Statistically stable features at all scales must be relatable at various scale levels Figurally Relevant Spatial Scale Levels: Primitives and Neighbors Multi-object complex Individual object = multiple figures in geom. rel’n to neighbors in relation to complex Individual figure = mesh of medial atoms subfigs in relation to neighbors in relation to object Figural section = multiple figural sections each centered at medial atom medial atoms in relation to neigbhors in relation to figure Figural section residue, more finely spaced, .. => multiple boundary sections (vertices) Boundary section vertices in relation to vertex neighbors in relation to figural section Boundary section more finely spaced, ... Multiscale Probability Leads to Trainable Probabilities If the total geometric representation z is at all scales or smallest scale, it is not stably trainable with attainable numbers of training cases, so multiscale Let zk be the geometric representation at scale level k Let zki be the ith geometric primitive at scale level k Let N(zki) be the neighbors of zki (at level k) Let P(zki) be the parent of zki (at level k-1) Probability via Markov random fields p(zki | P(zki), N(zki) ) Many trainable probabilities If p(zki rel. to P(zki), zki rel. to N(zki) ) Requires parametrized probabilities Multi-Scale-Level Image Analysis Geometry + Probability Multiscale critical for effectiveness with efficiency O(number of smallest scale primitives) Markov random field probabilistic basis Vs. methods working at small scale only or at global scale + small scale only Multi-Scale-Level Image Analysis via M-reps Thesis: multi-scale-level image analysis is particularly well supported by representation built around m-reps Intuitive, medically relevant scale levels Object-based positional and orientational correspondence Geometrically well suited to deformation Geometric Typicality Metrics Statistical Metrics Statistics/Probability Aspects : Principal component analysis Any shape, x, can be written as x = xmean + Pb + r log p(x) = f(b1, … bt,|r|2) x2 p1 b1 xmean xi x1 Visualizing & Measuring Global Deformation Shape Measurement Modes of shape variation across patients Measurement = z amount of each mode c = cmean + z1s1p1 c = cmean + z2s2p2 Statistics/Probability Aspects : Markov random fields (z1 … zn) p(zi | {zj, ji}) = p(zi | {zk : k a neighbour of i}) Suppose zT= (i. e., assume sparse covariance matrix) Need only evaluate O(n) terms to optimize p(z) or p(z | image) Can only evaluate p(zi), i.e., locally Interscale; within scale by locality Multiscale Geometry and Probability If z is at all scales or smallest scale, it is not stably trainable, so multiscale Let zk be the geometric rep’n at scale k Let zki be the ith geometric primitive at scale k Let N(zki) be the neighbors of zki Let P(zki) be the parent of zki Let C(zki) be the children of zki Probability via Markov random fields p(zki | P(zki), N(zki), C(zki) ) Many trainable probabilities Requires parametrized probabilities for training Examples with m-reps components p(zki | P(zki), N(zki), C(zki) ) z1 (necessarily global): similarity transform for body section z2i: similarity transform for the ith object Neighbors are adjacent (perhaps abutting) objects z3i: “similarity” transform for the ith figure of its object in its parent’s figural coordinates Neighbors are adjacent (perhaps abutting) figures z4i: medial atom transform for the ith medial atom Neighbors are adjacent medial atoms z5i: medial atom transform for the ith medial atom residue at finer scale (see next slide) z6i: boundary offset along medially implied normal for the ith boundary vertex Neighbors are adjacent vertices Multiscale Geometry and Probability for a Figure Geometrically smaller scale Interpolate (1st order) finer spacing of atoms Residual atom change, i.e., local coarse, global coarse resampled Probability At any scale, relates figurally homologous points Markov random field relating medial atom with its immediate neighbors at that scale its parent atom at the next larger scale and the corresponding position its children atoms fine, local Published Methods of Global Statistical Geometric Characterization in Medicine Global variability via principal component analysis on features globally, e.g., boundary points or landmarks, or global features, e.g., spherical harmonic coefficients for boundary Global difference via globally or on global features Globally based diagnosis via linear (or other) discriminant on features linear (or other) discriminant on features globally or on global features Example authors: [Bookstein][Golland] [Gerig] [Joshi] [Thompson & Toga][Taylor] Published Methods of Local Statistical Geometric Characterization A Local variability Local difference via principal component analysis on features globally or on global features, plus display of local properties of principal component via linear (or other) discriminant on global geometric primitives, plus display of local properties of discriminant direction On displacement vectors: signed, unsigned re inside/outside Outward, p < 0.05 p > 0.05 Inward, p < 0.05 R Example authors: [Gerig] [Golland] [Joshi] [Taylor] [Thompson & Toga] L Displacement significance: Schizophrenic vs. control hippocampus Shortcomings of Published Methods of Statistical Geometric Characterization Unintuitive Would like terms like bent, twisted, pimpled, constricted, elongated, extra figure Frequently nonlocal or local wrt global template Depends on getting correspondence to template correct Need where the differences are in object coordinates Which object, which figure, where in figure, where on boundary surface Requires infeasible number of training cases Due to too many random variables (features) Overcoming Shortcomings of Methods of Statistical Geometric Characterization Intuitive Figural (medial) representation provides terms like bent, twisted, pimpled, constricted, elongated, extra figure Local Hierarchy by scale level provides appropriate level of locality Object & figure based hierarchy yields intuitive locality and good positional correspondences Which object, which figure, where in figure, where on boundary surface Positional correspondences across training cases & scale levels Trainable by feasible number of cases Few features in residue between scale levels Relative to description at next larger scale level Relative to neighbors at same scale level Conclusions re Object Based Image Analysis Work at multiple levels of scale At each scale use representation appropriate for that scale At intermediate scales Represent medially Sense at (implied) boundary Papers at midag.cs.unc.edu/pubs/papers Extensions Variable topology jump diffusion (local shape) level set? Active shape Appearance Models and intensity ‘explaining’ the image iterative matching algorithm Recommended Readings For deformable sampled boundary models: T Cootes, A Hill, CJ Taylor (1994). Use of active shape models for locating structures in medical images. Image & Vision Computing 12: 355-366. For deformable parametrized boundary models: Kelemen, Gerig, et al For m-rep based shape: Pizer, Fritsch, et al, IEEE TMI, Oct. 1999 For 3D deformable m-reps: Joshi, Pizer, et al, IPMI 2001 (Springer LNCS 2082); Pizer, Joshi, et al, MICCAI 2001 (Springer LNCS 2208) Recommended Readings For Procrustes, landmark based deformation (Bookstein), shape space (Kendall): especially understandable in Dryden & Mardia, Statistical Shape Analysis For iterative conditional posterior, pixel primitive based shape: Grenander & Miller; Blake; Christensen et al