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    UPDATE: statismo models for elastic mesh registration in R

    24 Oct 2014

    UPDATE: 10/2016

    As the kernel interface has changed in July 2016 (read more here), I added the updated commands in the code section.

    I changed/fixed some stuff in the interaction between RvtkStatismo and my matching routines (gaussMatch and AmbergRegister) which are:

    These improvements allow for a controlled decrease in the model’s influence on the deformation the closer we get to the target shape.

    In case someone wants to rerun the examples from my earlier post, here are the updated parameters/commands:

    CAVEAT:

    You need to install latest version from master branch of mesheR, RvtkStatismo and Morpho !! 
    require(RvtkStatismo)
require(Rvcg)
require(Morpho)
require(mesheR)
require(rgl)
data(humface)
data(dummyhead)


### first create a model based on the reference


## mymod <- statismoModelFromRepresenter(dummyhead.mesh,kernel = list(c(50,50)),ncomp = 100) ## Old Interface
## new kernel interface as of 07/2016
GK <- GaussianKernel(50,50)
mymod <- statismoModelFromRepresenter(dummyhead.mesh,kernel=GK,ncomp = 100)

##now create an object of class BayesDeform

Bayes <- createBayes(mymod,sdmax=rep(4,30),ptValueNoise = 2,wt=1.5,shrinkfun = function(x,i){ x <- x*0.9^i})# we start off with a rather strong weight for the model with each iteration it will be 90% of the previous weight
## setup parameters for AmbergRegister
params <- list(iterations=30)
params <- append(params, list(
        # then let it increase from 0.2 to 0.6
        lambda=seq(from = 0.2,to=0.6,length.out = params$iterations),
        # treat \code{k} similar as \code{lambda}
        k=seq(from = 1,to=params$iterations,by=1),
        useiter=FALSE # iteratively deform dummyhead onto humface
        ))

## setup parameters for some additional rigid wiggling (ICP)

rigid <- list(iterations=60,subsample=200,rhotol=pi/2,uprange=0.3)#here we specify an overlap between reference and target of 30% 

## run the matching
map <- AmbergRegister(Bayes, humface, lm1=dummyhead.lm, lm2=humface.lm, iterations=params$iterations,k=params$k, lambda=params$lambda, useiter=params$useiter,rigid=rigid,visualize = T)

    Here is the output as movie:

    And here is the updated example matching Marcel’s Femur surfaces:

    
require(RvtkStatismo)
require(Rvcg)
require(Morpho)
require(mesheR)
require(rgl)

download.file(url="https://github.com/marcelluethi/statismo-shaperegistration/raw/master/data/VSD001_femur.vtk","./VSD001_femur.vtk",method = "w")
download.file(url="https://github.com/marcelluethi/statismo-shaperegistration/raw/master/data/VSD002_femur.vtk","./VSD002_femur.vtk",method = "w")
download.file(url="https://github.com/marcelluethi/statismo-shaperegistration/raw/master/data/VSD001-lm.csv","./VSD001-lm.csv",method = "w")
download.file(url="https://github.com/marcelluethi/statismo-shaperegistration/raw/master/data/VSD002-lm.csv","./VSD002-lm.csv",method = "w")

ref <- read.vtk("VSD001_femur.vtk")
tar <- read.vtk("VSD002_femur.vtk")

ref.lm <- as.matrix(read.csv("VSD001-lm.csv",row.names=1))
tar.lm <- as.matrix(read.csv("VSD002-lm.csv",row.names=1))
## mymod <- statismoModelFromRepresenter(ref,kernel=list(c(50,50)),ncomp = 100) ## Old Interface
## new kernel interface as of 07/2016
GK <- GaussianKernel(50,50)
mymod <- statismoModelFromRepresenter(ref,kernel=GK,ncomp = 100) 

Bayes <- createBayes(mymod,sdmax = rep(4,50),wt=1.5,shrinkfun = function(x,i){ x <- x*0.93^i})

## setup similarity and affine icps
similarity = list(iterations=10,rhotol=pi/2)
affine = list(iterations=10,rhotol=pi/2)

### run the matching
matchGP <- gaussMatch(Bayes,tar,lm1 = ref.lm,lm2=tar.lm,iterations = 50,sigma = 30,gamma=2,toldist = 30,angtol = pi/2,nh=100,visualize = T,similarity = similarity,affine = affine)

    Here is the output as movie:

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