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from ij.plugin.filter import GaussianBlur | |
from fiji.threshold import Auto_Local_Threshold as ALT | |
from ij.plugin.filter import ParticleAnalyzer as PA | |
from org.jfree.data.statistics import BoxAndWhiskerCalculator | |
from java.util import ArrayList, Arrays | |
import os | |
# size of juxtanuclear region. In pixels. | |
RIMSIZE = 15 | |
# image background is expected to be black. | |
Prefs.blackBackground = True | |
# verbose output | |
VERBOSE = False | |
root = '/Volumes/data/bio-it_centres_course/data/course' | |
filedapi = '--W00005--P00001--Z00000--T00000--dapi.tif' | |
nucpath = os.path.join(root, filedapi) | |
impN = IJ.openImage(nucpath) | |
class InstBWC(BoxAndWhiskerCalculator): | |
def __init__(self): | |
pass | |
def getOutlierBound(rt): | |
""" Analyzes the results of the 1st partcile analysis. | |
Since the dilation of nuclear perimeter often causes | |
overlap of neighboring neculeus 'terrirories', such nucleus | |
are discarded from the measurements. | |
Small nucelei are already removed, but since rejection of nuclei depends on | |
standard outlier detection method, outliers in both smaller and larger sizes | |
are discarded. | |
""" | |
area = rt.getColumn(rt.getColumnIndex('Area')) | |
circ = rt.getColumn(rt.getColumnIndex("Circ.")) | |
arealist = ArrayList(Arrays.asList(area.tolist())) | |
circlist = ArrayList(Arrays.asList(circ.tolist())) | |
bwc = InstBWC() | |
ans = bwc.calculateBoxAndWhiskerStatistics(arealist) | |
#anscirc = bwc.calculateBoxAndWhiskerStatistics(circlist) | |
if (VERBOSE): | |
print ans.toString() | |
print ans.getOutliers() | |
q1 = ans.getQ1() | |
q3 = ans.getQ3() | |
intrange = q3 - q1 | |
outlier_offset = intrange * 1.5 | |
return q1, q3, outlier_offset | |
def backgroundSubtraction(imp): | |
""" subtract background, Cihan's method. | |
see Simpson(2007) | |
""" | |
impstats = imp.getProcessor().getStatistics() | |
imp.getProcessor().setThreshold(impstats.min, impstats.mean, ImageProcessor.RED_LUT) | |
measOpt = ImageStatistics.MEAN + ImageStatistics.LIMIT | |
impstats = ImageStatistics.getStatistics(imp.getProcessor(), measOpt, None) | |
backlevel = impstats.mean | |
imp.getProcessor().resetThreshold() | |
imp.getProcessor().subtract(backlevel) | |
print imp.getTitle(), " : background intensity - ", backlevel | |
return backlevel | |
def nucleusSegmentation(imp2): | |
""" Segmentation of nucleus image. | |
Nucleus are selected that: | |
1. No overlapping with dilated regions | |
2. close to circular shape. Deformed nuclei are rejected. | |
Outputs a binary image. | |
""" | |
#Convert to 8bit | |
ImageConverter(imp2).convertToGray8() | |
#blur slightly using Gaussian Blur | |
radius = 2.0 | |
accuracy = 0.01 | |
GaussianBlur().blurGaussian( imp2.getProcessor(), radius, radius, accuracy) | |
# Auto Local Thresholding | |
imps = ALT().exec(imp2, "Bernsen", 15, 0, 0, True) | |
imp2 = imps[0] | |
#ParticleAnalysis 0: prefiltering by size and circularity | |
rt = ResultsTable() | |
paOpt = PA.CLEAR_WORKSHEET +\ | |
PA.SHOW_MASKS +\ | |
PA.EXCLUDE_EDGE_PARTICLES +\ | |
PA.INCLUDE_HOLES #+ \ | |
# PA.SHOW_RESULTS | |
measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY | |
MINSIZE = 20 | |
MAXSIZE = 10000 | |
pa0 = PA(paOpt, measOpt, rt, MINSIZE, MAXSIZE, 0.8, 1.0) | |
pa0.setHideOutputImage(True) | |
pa0.analyze(imp2) | |
imp2 = pa0.getOutputImage() # Overwrite | |
imp2.getProcessor().invertLut() | |
impNuc = imp2.duplicate() ## for the ring. | |
#impNuc = Duplicator().run(imp2) | |
#Dilate the Nucleus Area | |
## this should be 40 pixels in Cihan's method, but should be smaller. | |
rf = RankFilters() | |
rf.rank(imp2.getProcessor(), RIMSIZE, RankFilters.MAX) | |
#Particle Analysis 1: get distribution of sizes. | |
paOpt = PA.CLEAR_WORKSHEET +\ | |
PA.SHOW_NONE +\ | |
PA.EXCLUDE_EDGE_PARTICLES +\ | |
PA.INCLUDE_HOLES #+ \ | |
# PA.SHOW_RESULTS | |
measOpt = PA.AREA + PA.STD_DEV + PA.SHAPE_DESCRIPTORS + PA.INTEGRATED_DENSITY | |
rt1 = ResultsTable() | |
MINSIZE = 20 | |
MAXSIZE = 10000 | |
pa = PA(paOpt, measOpt, rt1, MINSIZE, MAXSIZE) | |
pa.analyze(imp2) | |
#rt.show('after PA 1') | |
#particle Analysis 2: filter nucleus by size and circularity. | |
#print rt1.getHeadings() | |
if (rt1.getColumnIndex('Area') > -1): | |
q1, q3, outlier_offset = getOutlierBound(rt1) | |
else: | |
q1 = MINSIZE | |
q3 = MAXSIZE | |
outlier_offset = 0 | |
print imp2.getTitle(), ": no Nucleus segmented,probably too many overlaps" | |
paOpt = PA.CLEAR_WORKSHEET +\ | |
PA.SHOW_MASKS +\ | |
PA.EXCLUDE_EDGE_PARTICLES +\ | |
PA.INCLUDE_HOLES #+ \ | |
# PA.SHOW_RESULTS | |
rt2 = ResultsTable() | |
pa = PA(paOpt, measOpt, rt2, q1-outlier_offset, q3+outlier_offset, 0.8, 1.0) | |
pa.setHideOutputImage(True) | |
pa.analyze(imp2) | |
impDilatedNuc = pa.getOutputImage() | |
#filter original nucleus | |
filteredNuc = ImageCalculator().run("AND create", impDilatedNuc, impNuc) | |
return filteredNuc | |
imp2 = impN.duplicate() | |
impfilteredNuc = nucleusSegmentation(imp2) | |
impfilteredNuc.show() |
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