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| | Nice site. Thank you., http://hapuga.infos.st/fairfield-count/index.html organizational behavior issue, ajjcx, http://maerka.fr.nf/art-nouveau/index.html minnesota west college, 03751, | | Nice site. Thank you., http://hapuga.infos.st/fairfield-count/index.html organizational behavior issue, ajjcx, http://maerka.fr.nf/art-nouveau/index.html minnesota west college, 03751, |
| | | | |
| − | =K-Medoids Fiber Clustering=
| + | Very good site. Thanks!, http://sallar.myip.org/desktop-publish/index.html nightlife sim2, aqpzdy, http://gammapoll.aelita.fr/cyprus-larn/index.html matrix capital bank, jdg, http://kappa.infos.st/cozumel-hotel/index.html otay border crossing, =)), http://kiki.mydns.hu/akon-ft/index.html texas state agency, 6750, http://arkan.biz.st/backyard-boxing/index.html ncaa basketball coach, pidb, |
| − | <pre>
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| − | XML = """<?xml version="1.0" encoding="utf-8"?>
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| − | <executable>
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| − | | |
| − | <category>Demo Scripted Modules</category>
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| − | <title>K-Medoids fiber clustering</title>
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| − | <description>
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| − | Fiber Clustering simple K-Medoids
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| − | </description>
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| − | <version>1.0</version>
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| − | <documentation-url></documentation-url>
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| − | <license></license>
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| − | <contributor>Demian Wassermann</contributor>
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| − | | |
| − | <parameters>
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| − | <label>IO</label>
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| − | <description>Input/output parameters</description>
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| − | | |
| − | <geometry type = "fiberbundle" >
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| − | <name>inputFiberBundle</name>
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| − | <longflag>inputFiberBundle</longflag>
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| − | <label>Input Fiber Bundle</label>
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| − | <channel>input</channel>
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| − | <description>Input bundle</description>
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| − | </geometry>
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| − | | |
| − | <geometry >
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| − | <name>outputFiberBundle</name>
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| − | <longflag>outputFiberBundle</longflag>
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| − | <label>Output Fiber Bundle</label>
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| − | <channel>output</channel>
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| − | <description>Clustered bundle</description>
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| − | </geometry>
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| − | | |
| − | <integer>
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| − | <name>numberOfClusters</name>
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| − | <longflag>numberOfClusters</longflag>
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| − | <label>Number of clusters for K-Medoids</label>
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| − | <default>5</default>
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| − | <step>1</step>
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| − | <channel>input</channel>
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| − | <constraints>
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| − | <minimum>2</minimum>
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| − | <maximum>100</maximum>
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| − | </constraints>
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| − | </integer>
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| − | | |
| − | </parameters>
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| − | <parameters advanced="true">
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| − | <label>Advanced</label>
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| − | <integer>
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| − | <name>subsampling</name>
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| − | <longflag>subsampling</longflag>
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| − | <label>Number of fiber points to keep</label>
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| − | <default>15</default>
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| − | <step>1</step>
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| − | <channel>input</channel>
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| − | <constraints>
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| − | <minimum>2</minimum>
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| − | <maximum>1000</maximum>
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| − | </constraints>
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| − | </integer>
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| − | <integer>
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| − | <name>minimumFiberLength</name>
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| − | <longflag>minimumFiberLength</longflag>
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| − | <label>minimum fiber length to consider valid</label>
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| − | <default>15</default>
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| − | <step>1</step>
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| − | <channel>input</channel>
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| − | <constraints>
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| − | <minimum>2</minimum>
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| − | <maximum>1000</maximum>
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| − | </constraints>
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| − | </integer>
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| − | </parameters>
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| − | | |
| − | </executable>
| |
| − | """
| |
| − | | |
| − | from Slicer import slicer
| |
| − | import numpy
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| − | | |
| − | # Warning, this example needs the package Pycluster
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| − | # http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm#pycluster
| |
| − | import Pycluster
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| − | | |
| − | | |
| − | | |
| − | | |
| − | def Execute (inputFiberBundle="", outputFiberBundle="", numberOfClusters=2, subsampling=15, minimumFiberLength=15 ):
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| − |
| |
| − | scene = slicer.MRMLScene
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| − | | |
| − | inputFiberBundleNode = scene.GetNodeByID(inputFiberBundle)
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| − | outputFiberBundleNode = scene.GetNodeByID(outputFiberBundle)
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| − | | |
| − | | |
| − | #Prepare the output fiber bundle and the Arrays for the atlas labeling and clustering
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| − | | |
| − | clusters = setupTheOutputNode( inputFiberBundleNode, outputFiberBundleNode )
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| − | clusters_array = clusters.ToArray().squeeze()
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| − | | |
| − | #Get the fibers form the Polydata and susbsample them
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| − | fibers, lines = fibers_from_vtkPolyData( inputFiberBundleNode.GetPolyData(), minimumFiberLength )
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| − | | |
| − | subsampledFibers = []
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| − | | |
| − | for fiber in fibers:
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| − | subsampledFibers.append( fiber[::max( len(fiber)/subsampling, len(fiber) ) ] )
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| − | | |
| − | | |
| − | #Generate the distance matrix
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| − | distanceMatrix = numpy.zeros( (len(fibers),len(fibers)), dtype=float )
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| − | for i in xrange( len(fibers) ):
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| − | for j in xrange( 0, i):
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| − | distanceMatrix[ i, j ] = dist_hausdorff_min( subsampledFibers[i], subsampledFibers[j] )
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| − | distanceMatrix[ j, i ] = distanceMatrix[ i, j ]
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| − | | |
| − | | |
| − | | |
| − | | |
| − | #Perform the clustering
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| − | fiberClusters = renumberLabels(Pycluster.kmedoids( distanceMatrix, numberOfClusters, npass=100 )[0])
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| − | print fiberClusters
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| − | clusters_array[:]=0
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| − | | |
| − | | |
| − | for i in xrange(len(lines)):
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| − | clusters_array[ lines[i] ] = fiberClusters[i]
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| − | | |
| − | clusters.Modified()
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| − | | |
| − | | |
| − | dist2 = lambda i,j : numpy.sqrt(((i-j)**2).sum(j.ndim-1))
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| − | dist_hausdorff_asym_mean = lambda i,j: numpy.apply_along_axis( lambda k: dist2(k,j).min(), 1,i).mean()
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| − | dist_hausdorff_min = lambda i,j : numpy.min(dist_hausdorff_asym_mean(i,j),dist_hausdorff_asym_mean(j,i))
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| − | | |
| − | | |
| − | | |
| − | def fibers_from_vtkPolyData(vtkPolyData, minimumFiberLength):
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| − | #Fibers and Lines are the same thing
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| − | | |
| − | lines = vtkPolyData.GetLines().GetData().ToArray().squeeze()
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| − | points = vtkPolyData.GetPoints().GetData().ToArray()
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| − | | |
| − | fibersList = []
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| − | linesList = []
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| − | actualLineIndex = 0
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| − | numberOfFibers = vtkPolyData.GetLines().GetNumberOfCells()
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| − | for l in xrange( numberOfFibers ):
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| − | if lines[actualLineIndex]>minimumFiberLength:
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| − | fibersList.append( points[ lines[actualLineIndex+1: actualLineIndex+lines[actualLineIndex]+1] ] )
| |
| − | linesList.append( lines[actualLineIndex+1: actualLineIndex+lines[actualLineIndex]+1] )
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| − | actualLineIndex += lines[actualLineIndex]+1
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| − | | |
| − | return fibersList, linesList
| |
| − | | |
| − | def setupTheOutputNode( inputFiberBundleNode, outputFiberBundleNode ):
| |
| − | if ( outputFiberBundleNode.GetPolyData()==[] ):
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| − | outputFiberBundleNode.SetAndObservePolyData(slicer.vtkPolyData())
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| − | | |
| − | outputPolyData = outputFiberBundleNode.GetPolyData()
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| − | outputPolyData.SetPoints( inputFiberBundleNode.GetPolyData().GetPoints() )
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| − | outputPolyData.SetLines( inputFiberBundleNode.GetPolyData().GetLines() )
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| − | outputPolyData.Update()
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| − | | |
| − | | |
| − | clusters = outputFiberBundleNode.GetPolyData().GetPointData().GetScalars('Cluster')
| |
| − | if (clusters==[] or clusters.GetNumberOfTuples()!=outputPolyData.GetPoints().GetNumberOfPoints() ):
| |
| − | clusters = slicer.vtkUnsignedIntArray()
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| − | clusters.SetNumberOfComponents(1)
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| − | clusters.SetNumberOfTuples( outputPolyData.GetPoints().GetNumberOfPoints() )
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| − | clusters.SetName('Cluster')
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| − | outputPolyData.GetPointData().AddArray( clusters )
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| − | | |
| − | return clusters
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| − | | |
| − | def renumberLabels(labelArray):
| |
| − | newLabeling=[]
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| − | for a in labelArray:
| |
| − | if not(a in newLabeling):
| |
| − | newLabeling.append(a)
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| − | | |
| − | newLabelArray=labelArray.copy()
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| − | for i in range(len(labelArray)):
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| − | newLabelArray[i]=newLabeling.index(labelArray[i])+1
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| − | | |
| − | return newLabelArray
| |
| − | </pre>
| |
