splitNiftiROIs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nibabel as nib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'E:/Celine/Ferret_b2n/F01_Adult/'\n",
    "fbizley = 'Bizley2009'\n",
    "niigz = '.nii.gz'\n",
    "\n",
    "imgbiz = nib.load(path+fbizley+niigz)\n",
    "databiz = imgbiz.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 21 22 23]\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "labels = np.unique(databiz)\n",
    "nbparcels = len(np.unique(databiz))-1 # -1 to remove the background\n",
    "print labels[1:]\n",
    "print nbparcels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in labels[1:]:\n",
    "    dataedited = np.zeros(databiz.shape,dtype=int)\n",
    "    for j in range(databiz.shape[0]):\n",
    "        for k in range(databiz.shape[1]):\n",
    "            dataedited[j,k]=[0 if e != i else e for e in databiz[j,k,:]]\n",
    "            dataedited[j,k]=[1 if e == i else e for e in dataedited[j,k,:]]\n",
    "    imgedited = nib.Nifti1Image(dataedited,affine=imgbiz.affine,header=imgbiz.header,extra=imgbiz.extra,file_map=imgbiz.file_map)\n",
    "    nib.save(imgedited, path+'Bizley2009/bizley_'+str(i)+'.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : \n",
      "db_name         : \n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : \n",
      "dim_info        : 0\n",
      "dim             : [  3 160 120  80   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : uint8\n",
      "bitpix          : 8\n",
      "slice_start     : 0\n",
      "pixdim          : [-1.          0.23999999  0.23999999  0.23999999  0.          1.          1.\n",
      "  1.        ]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 10\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : BrainBox, 20 August 2016\n",
      "aux_file        : \n",
      "qform_code      : unknown\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : 0.0\n",
      "qoffset_y       : 0.0\n",
      "qoffset_z       : 0.0\n",
      "srow_x          : [  0.          -0.23999999   0.          14.15999985]\n",
      "srow_y          : [  0.23999999   0.           0.         -18.95999908]\n",
      "srow_z          : [ 0.          0.         -0.23999999  9.59999943]\n",
      "intent_name     : \n",
      "magic           : n+1\n"
     ]
    }
   ],
   "source": [
    "print imgbiz.header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "img = nib.load(path+'mask_to_dwi'+niigz)\n",
    "data = img.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : \n",
      "db_name         : \n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : r\n",
      "dim_info        : 0\n",
      "dim             : [  3 160 120  80   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : float32\n",
      "bitpix          : 32\n",
      "slice_start     : 0\n",
      "pixdim          : [-1.          0.23999999  0.23999999  0.23999999  0.          0.          0.\n",
      "  0.        ]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 2\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : \n",
      "aux_file        : \n",
      "qform_code      : aligned\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 0.707106769085\n",
      "qoffset_x       : -0.0\n",
      "qoffset_y       : -0.0\n",
      "qoffset_z       : 0.0\n",
      "srow_x          : [  8.21485013e-09  -2.39999995e-01  -0.00000000e+00  -0.00000000e+00]\n",
      "srow_y          : [  2.39999995e-01   8.21485013e-09   0.00000000e+00  -0.00000000e+00]\n",
      "srow_z          : [ 0.          0.         -0.23999999  0.        ]\n",
      "intent_name     : \n",
      "magic           : n+1\n"
     ]
    }
   ],
   "source": [
    "print img.header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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