We describe a method for inferring tree-like vascular structures from 2D imagery. A Markov Chain Monte Carlo (MCMC) algorithm is employed to produce approximate samples from the posterior distribution given local feature estimates, derived from likelihood
Fig.2.MovesusedinMCMCsimulationontrees.iterative,EM-typealgorithmtomaximiselikelihood
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wherethedataarewindowedwithacosinewindow,whosesizeistwicetheblockwidthatagivenscale,togivethedatausedintheestimator.Theindexdenotesiterationnumber;typically4-5iterationsaresuf- cienttogiveaccurateestimates.Figures3(b)-(c)showreconstructionsusingthe2DGaussiansineachblock(atcorrespondingblocksizes)basedontheMLfeatureesti-matesatblocksizesofandrespectively.Clearly,atlowerspatialresolutions,themodelcannoteas-ilydescribethepresenceofmultiplevesselswithinthewin-dow,suchasoccuratbifurcations,andtheresultinglow-amplitude,isotropicGaussiansarelocallythe‘best’de-scriptionoftheseregions.However,theseblockscanbemodelledaccuratelyathigherspatialresolutions.Thesec-ondsetofimagesshowshowthelocalestimatesfromdif-ferentwindowscalesisusedinacoarse- nestochasticsim-ulation,inordertogetaBayesianestimateoftheforest
structure.Afterthe rst200iterations,thescaleishalvedandtheappropriatelocalfeatureestimatesareusedtoguidethesampler;after3000,thescaleishalvedagain,asitisafter6000iterations,atwhichpoint,thehighestspatialres-olutionisreached.Thisapproachhasbeenfoundtospeedconvergencetotheequilibriumdistribution,whileavoidingbecomingtrappedinlocalmodes,inasimilarmannertomanycoarse- nealgorithms.Notethatoneiterationcon-sistsofthegenerationandacceptanceofasingleproposal(for‘editing’thetree).Inotherwords,100000iterationsiscomparable,intermsofcomputation,toasinglescanthroughtheimage.Ithasbeennotedfromexperimentsthatequilibriumisreachedinapproximately50000iterations,acomparativelylowburdencomputationally.
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