Thursday, February 17, 2011

Segmentation of a complex metaphase with pymorph

Part of the original 16bits TIF image

Labelled image displayed with pylab
The mean gray level value is also calculated for each labelled region with pymorph.grain:
[ 1301.88575849    23.00804348   704.34775673   629.5625966   1410.2556391
   871.05686546   946.11368209   841.65226782   860.98429319  1526.48712121
   705.22321429   608.3359841    929.86325301   858.11372549  1000.02900378
   831.46130952   717.89433962   790.23236741   919.48754448   603.34122288
   830.35550936  1325.0239521    843.45842697   880.9575       864.25319693
   970.12876254   901.85921959   914.75644699   902.17860963   864.04659498
   960.92638037   892.95342466   946.98757358  1119.16554163   835.44263629
   872.87977369   838.04045734   734.5129683   1147.49576784   995.61397059
   839.4835443    823.52125984   795.88679245   898.91109422   748.07751938
   635.60344828]
the python code used for that result:

from scipy import ndimage as ND
import scipy
import numpy as np
import readmagick
import mahotas
import pymorph
import pylab
import os
def SegmentMetaphase(image):
    #print "def image:",image.mean()
    #print "def background:",back.mean()
    #print "def noback:",im.mean()
    noBack=RemoveModalBackground(image)
    print "from Segmeta noBack:",noBack.min(),noBack.mean()
    blurLowRes=ND.filters.gaussian_filter(noBack,13)
    blurHiRes=ND.filters.gaussian_filter(noBack,1)
    midPass=pymorph.subm(blurHiRes,0.70*blurLowRes)
    bin=(midPass>1.5*midPass.mean())
    binLowRes=pymorph.open(bin,pymorph.sedisk(4))
    binLR=pymorph.edgeoff(binLowRes)
    blur=ND.filters.gaussian_filter(noBack,9)
    hiPass=pymorph.subm(noBack,1.0*blur)
    gradLowRes=pymorph.gradm(noBack)
    #build seeds for watershed
    bSeeds1=(hiPass>hiPass.mean())
    bSeeds2=scipy.logical_and(bSeeds1,binLR)
    #bSeeds3=ND.binary_fill_holes(bSeeds2)
    bSeeds4=ND.binary_opening(bSeeds2,iterations=4)
    outline=mahotas.bwperim(bin, n=4)
    #display=pymorph.overlay(image,outline)#Hugly
    markers,nr_obj=ND.label(bSeeds4)
    labeled=mahotas.cwatershed(gradLowRes, markers, Bc=None, return_lines=False)
    return labeled
def RemoveModalBackground(image):
    mode=ModalValue(image)
    back = np.zeros(image.shape, image.dtype)
    back.fill(mode)
    #print "def background:",back.mean()
    im=pymorph.subm(image,back)
    return im
def ModalValue(image):
    '''look for the modal value of an image'''
    #print image.dtype
    if image.dtype=="uint8":
        depthmax=255
        print "8bits"
    if image.dtype=="uint16":
        depthmax=65535
        print "16bits"
    histo=mahotas.fullhistogram(image)
    countmax=histo.max()
    print "countmax:",countmax
    print "image max",image.max()
    mig=image.min()#image min graylevel
    mag=image.max()#image max gray level
    mode=0
    countmax=0#occurence of a given grayscale
    print "mig=",mig,"  mag=",mag
    for i in range(mig,mag-1,1):
        test=histo[i]>countmax
        #print "test:",test,"histo(",i,")=", histo[i],"max",countmax
        if  test:
            countmax=histo[i]
            mode=i
            #print "mode",mode
    return mode
def BorderKill(imlabel):
    '''remove labelled objects touching the image border'''
    #from pythonvision.org
    #pymorph.edgeoff should do the same
    whole = mahotas.segmentation.gvoronoi(imlabel)
    borders = np.zeros(imlabel.shape, np.bool)
    borders[ 0,:] = 1
    borders[-1,:] = 1
    borders[:, 0] = 1
    borders[:,-1] = 1
    at_border = np.unique(imlabel[borders])
    for obj in at_border:
        whole[whole == obj] = 0
    return whole
user=os.path.expanduser("~")
workdir=os.path.join(user,"Applications","ImagesTest","JPP60-3","DAPI")
file="1.tif"
complete_path=os.path.join(workdir,file)
if __name__ == "__main__":
    #print complete_path
    dapi=readmagick.readimg(complete_path)
    readmagick.writeimg(dapi,os.path.join(workdir,"dapi.png"))
    NoBackIm=RemoveModalBackground(dapi)
    dapilab1=SegmentMetaphase(dapi)
    readmagick.writeimg(dapilab1,os.path.join(workdir,"dapilabel.png"))
    print pymorph.grain(NoBackIm,dapilab1, 'mean', option='data')
    pylab.imshow(dapilab1)
    pylab.show()
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