jeanpat/extractParticles.ipynb

## extractParticles.ipynb
{
 "metadata": {
  "name": "Extracting particles from an image- Toy model"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "extractParticles()"
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": " Extracting particles from a labelled image: a toy model with numpy"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Image segmentation can be performed by an image labelling process. Once segmented, it could necessary to isolate in independant images the labelled regions. Think of chromosomes in a metaphase image, if resolving chromosomes overlapps or karyotyping must be performed.\n\nThe following script takes \"two images\", (in fact two small numpy arrays:img is supposed to be a gray level image and lab, the label image which could be obtained after segmentation) and returns two particles, that is two images corresponding to the labelled regions (1 or 2. 0 corresponds to the background):"
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "First let's create two image models: A grey level image as follow"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "img=np.array([\\\n[75,167,181,105,33,5,2,2],\\\n[72,163,193,136,54,10,1,2],\\\n[54,123,154,118,51,11,2,2],\\\n[23,54,69,58,33,18,11,4],\\\n[5,15,27,43,62,69,53,23],\\\n[5,24,61,104,147,162,124,54],\\\n[10,54,128,186,220,222,168,72],\\\n[14,72,169,230,247,238,177,76]],np.int16)\npylab.imshow(img, interpolation='nearest')\nshow()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x3125190>"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "lab=np.array([\\\n[0,1,1,1,0,0,0,0],\\\n[0,1,1,1,1,0,0,0],\\\n[0,1,1,1,1,1,1,0],\\\n[0,1,2,2,0,0,1,0],\\\n[0,0,2,0,0,2,2,2],\\\n[0,0,2,2,2,2,2,2],\\\n[0,0,2,2,2,2,2,2],\\\n[0,2,2,2,2,2,2,2]],np.int16)\npylab.imshow(lab, interpolation='nearest')\nshow()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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waOHKM8AgwgYMImzAIMIGDCJswCDCBgz6P69STSHCl6tTAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x3778b50>"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#\n#March14, 2011, A script to extract regions (particles) from an array (image)\n#Jean-Patrick Pommier\n#\nimport numpy as np\nimport os\ndef extractParticles(grayIm,labIm):\n    ''' give a grayscaled and a labelled image, extract the segmented particles\n    ,returns a list of flattened particles'''\n    #grayIm and labIm should have the same size\n    def unflattenParticles(flatParticleList):\n        '''take a list of flat particles and unflat them to yield an image'''\n        unflatList=[]\n        lenFlatList=len(flatParticleList)\n        for i in range(0,lenFlatList):\n            #get the i particle:current Particle\n            curPart=flatParticleList[i]#current particle\n            #x values(col) are stored in the third col (3-1)\n            colmax=curPart[:,2].max()\n            colmin=curPart[:,2].min()\n            #y values(li) are stored in the fourth col (4-1)\n            limax=curPart[:,3].max()\n            limin=curPart[:,3].min()\n            unflatIm=np.zeros((limax-limin+1,colmax-colmin+1),np.int16)\n            #number of pixels in the particle\n            nbPixel=len(curPart[:,1])#count how many lines at col=1\n            for line in range(0,nbPixel):\n                col=curPart[line,2]\n                li=curPart[line,3]\n                pixVal=curPart[line,1]\n                unflatIm[li-limin,col-colmin]=pixVal\n            unflatList.append(unflatIm)\n        return unflatList\n                   \n    sx=grayIm.shape[0]\n    sy=grayIm.shape[1]\n    #flatten grayIm\n    fg=grayIm.flatten()\n    fl=labIm.flatten()\n    labmax=fl.max()\n    #print fg\n    #print fl\n    #build two 2D array containing x and y\n    #of each pixel of the grayIm\n    ax=np.zeros((sx,sy),np.int16)\n    ay=np.zeros((sx,sy),np.int16)\n    #vectorization with numpy may be \n    #more efficient than two loops\n    for j in range(0,sy):\n        for i in range(0,sx):\n            ax[i,j]=j#filling ax with x=col\n            ay[i,j]=i#filling ay with y values y=li\n    #flat arrays of coordinates\n    fax=ax.flatten()\n    fay=ay.flatten()\n    #1D merge graylevel, label and coordinates \n    #in one 1D array of 4-uplet\n    extract=np.vstack((fl,fg,fax,fay))\n    #transpose to watch it easily\n    eT=extract.T\n    #create a list of flatten particles\n    #labIndex takes the value from 1 (the first particle to labmax the\\\n    #label of the last particle\n    flatParticleList=[]#from Matthieu Brucher\n    for labIndex in range(1,labmax+1):\n        flatParticleList.append(eT[eT[:,0]==labIndex])#from Matthieu Brucher\n    return unflattenParticles(flatParticleList)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Extract particles with two methods: find_object() or extractParticles()"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "Particles=extractParticles(img,lab)\n#\nfrom scipy import ndimage as nd\n\nseg2 = nd.find_objects(lab==2)\nseg1 = nd.find_objects(lab==1)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Let's display the segmented particles"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "subplot(242, xticks=[],yticks=[])\nimshow(lab, interpolation='nearest')\n\nsubplot(243, xticks=[],yticks=[])\nimshow(Particles[0]>0, interpolation='nearest')\n\nsubplot(244, xticks=[],yticks=[])\nimshow(Particles[1]>0, interpolation='nearest')\n\nsubplot(247, xticks=[],yticks=[])\nimshow(lab[seg1[0]], interpolation='nearest')\n\nsubplot(248, xticks=[],yticks=[])\nimshow(lab[seg2[0]], interpolation='nearest')\n\nshow()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAADGCAYAAADbnZU/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": "<matplotlib.figure.Figure at 0x37796d0>"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The two lower images correspond to particles extraction with scipy.ndimage.find_objects(). The two upper images correspond to particles extracted with extractParticles().\nscipy.ndimage.find_objects() is not capable to isolate two touching particles probably because it uses a bouding box whereas extractParticles() relies only on particle label.\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "Extracting particles from an image- Toy model"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "heading",
	"level": 1,
	"metadata": {},
	"source": "extractParticles()"
	},
	{
	"cell_type": "heading",
	"level": 2,
	"metadata": {},
	"source": " Extracting particles from a labelled image: a toy model with numpy"
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "Image segmentation can be performed by an image labelling process. Once segmented, it could necessary to isolate in independant images the labelled regions. Think of chromosomes in a metaphase image, if resolving chromosomes overlapps or karyotyping must be performed.\n\nThe following script takes \"two images\", (in fact two small numpy arrays:img is supposed to be a gray level image and lab, the label image which could be obtained after segmentation) and returns two particles, that is two images corresponding to the labelled regions (1 or 2. 0 corresponds to the background):"
	},
	{
	"cell_type": "heading",
	"level": 3,
	"metadata": {},
	"source": "First let's create two image models: A grey level image as follow"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "img=np.array([\\\n[75,167,181,105,33,5,2,2],\\\n[72,163,193,136,54,10,1,2],\\\n[54,123,154,118,51,11,2,2],\\\n[23,54,69,58,33,18,11,4],\\\n[5,15,27,43,62,69,53,23],\\\n[5,24,61,104,147,162,124,54],\\\n[10,54,128,186,220,222,168,72],\\\n[14,72,169,230,247,238,177,76]],np.int16)\npylab.imshow(img, interpolation='nearest')\nshow()",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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enq6Kigo1NDRo4cKFvmTdKiUlRZI0fvx4zZ8/X0eP+nMhfigUUnJysoqKiiRJCxYsUHOz\n/99niH0H4IGzXuzCwkKdOnVK586dU1dXl3bt2qXS0lLbY/jGGKOVK1dq6tSpWrdune95bW1tN08X\nOzo6VFNTo5ycHF+yNm7cqJaWFp05c0bV1dWaMWOGdu/e7UvWDZ2dners7JTUdxZUU1OjrKwsX7JC\noZBCodDNMtfW1iojI8OXrFv5cgdgT96CG6D9+/ebrKws88ADD5iNGzf6mvXEE0+YCRMmmKSkJJOa\nmmqef/55X/M+/PBDEwgETF5ensnPzzf5+fnmjTfe8C2vubnZ5Ofnm7y8PBMOh80zzzzjW9at3n//\nfSvvin/55ZcmLy/P5OXlmSlTppinn37a17ympiZTUFBgMjIyTGlpqbl48aKvee3t7WbcuHHm8uXL\nnh7Xk2vFAdxduPIMcBDFBhxEsQEHUWzAQRQbcBDFBhz0/2X5mgJ2QrbZAAAAAElFTkSuQmCC\n",
	"text": "<matplotlib.figure.Figure at 0x3125190>"
	}
	],
	"prompt_number": 18
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "lab=np.array([\\\n[0,1,1,1,0,0,0,0],\\\n[0,1,1,1,1,0,0,0],\\\n[0,1,1,1,1,1,1,0],\\\n[0,1,2,2,0,0,1,0],\\\n[0,0,2,0,0,2,2,2],\\\n[0,0,2,2,2,2,2,2],\\\n[0,0,2,2,2,2,2,2],\\\n[0,2,2,2,2,2,2,2]],np.int16)\npylab.imshow(lab, interpolation='nearest')\nshow()",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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waOHKM8AgwgYMImzAIMIGDCJswCDCBgz6P69STSHCl6tTAAAAAElFTkSuQmCC\n",
	"text": "<matplotlib.figure.Figure at 0x3778b50>"
	}
	],
	"prompt_number": 43
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "#\n#March14, 2011, A script to extract regions (particles) from an array (image)\n#Jean-Patrick Pommier\n#\nimport numpy as np\nimport os\ndef extractParticles(grayIm,labIm):\n ''' give a grayscaled and a labelled image, extract the segmented particles\n ,returns a list of flattened particles'''\n #grayIm and labIm should have the same size\n def unflattenParticles(flatParticleList):\n '''take a list of flat particles and unflat them to yield an image'''\n unflatList=[]\n lenFlatList=len(flatParticleList)\n for i in range(0,lenFlatList):\n #get the i particle:current Particle\n curPart=flatParticleList[i]#current particle\n #x values(col) are stored in the third col (3-1)\n colmax=curPart[:,2].max()\n colmin=curPart[:,2].min()\n #y values(li) are stored in the fourth col (4-1)\n limax=curPart[:,3].max()\n limin=curPart[:,3].min()\n unflatIm=np.zeros((limax-limin+1,colmax-colmin+1),np.int16)\n #number of pixels in the particle\n nbPixel=len(curPart[:,1])#count how many lines at col=1\n for line in range(0,nbPixel):\n col=curPart[line,2]\n li=curPart[line,3]\n pixVal=curPart[line,1]\n unflatIm[li-limin,col-colmin]=pixVal\n unflatList.append(unflatIm)\n return unflatList\n \n sx=grayIm.shape[0]\n sy=grayIm.shape[1]\n #flatten grayIm\n fg=grayIm.flatten()\n fl=labIm.flatten()\n labmax=fl.max()\n #print fg\n #print fl\n #build two 2D array containing x and y\n #of each pixel of the grayIm\n ax=np.zeros((sx,sy),np.int16)\n ay=np.zeros((sx,sy),np.int16)\n #vectorization with numpy may be \n #more efficient than two loops\n for j in range(0,sy):\n for i in range(0,sx):\n ax[i,j]=j#filling ax with x=col\n ay[i,j]=i#filling ay with y values y=li\n #flat arrays of coordinates\n fax=ax.flatten()\n fay=ay.flatten()\n #1D merge graylevel, label and coordinates \n #in one 1D array of 4-uplet\n extract=np.vstack((fl,fg,fax,fay))\n #transpose to watch it easily\n eT=extract.T\n #create a list of flatten particles\n #labIndex takes the value from 1 (the first particle to labmax the\\\n #label of the last particle\n flatParticleList=[]#from Matthieu Brucher\n for labIndex in range(1,labmax+1):\n flatParticleList.append(eT[eT[:,0]==labIndex])#from Matthieu Brucher\n return unflattenParticles(flatParticleList)\n",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 14
	},
	{
	"cell_type": "heading",
	"level": 3,
	"metadata": {},
	"source": "Extract particles with two methods: find_object() or extractParticles()"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "Particles=extractParticles(img,lab)\n#\nfrom scipy import ndimage as nd\n\nseg2 = nd.find_objects(lab==2)\nseg1 = nd.find_objects(lab==1)",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 39
	},
	{
	"cell_type": "heading",
	"level": 3,
	"metadata": {},
	"source": "Let's display the segmented particles"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "subplot(242, xticks=[],yticks=[])\nimshow(lab, interpolation='nearest')\n\nsubplot(243, xticks=[],yticks=[])\nimshow(Particles[0]>0, interpolation='nearest')\n\nsubplot(244, xticks=[],yticks=[])\nimshow(Particles[1]>0, interpolation='nearest')\n\nsubplot(247, xticks=[],yticks=[])\nimshow(lab[seg1[0]], interpolation='nearest')\n\nsubplot(248, xticks=[],yticks=[])\nimshow(lab[seg2[0]], interpolation='nearest')\n\nshow()",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "display_data",
	"png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAADGCAYAAADbnZU/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
	"text": "<matplotlib.figure.Figure at 0x37796d0>"
	}
	],
	"prompt_number": 44
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": "The two lower images correspond to particles extraction with scipy.ndimage.find_objects(). The two upper images correspond to particles extracted with extractParticles().\nscipy.ndimage.find_objects() is not capable to isolate two touching particles probably because it uses a bouding box whereas extractParticles() relies only on particle label.\n"
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "",
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}