Watersheds Segmentation



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import matplotlib.pyplot as plt
%matplotlib inline
import SimpleITK as sitk
from myshow import myshow, myshow3d

# Download data to work on
from downloaddata import fetch_data as fdata



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img = sitk.ReadImage(fdata("cthead1.png"))
myshow(img)

Gradient Watersheds Segmentation



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sigma=img.GetSpacing()[0]
level=4



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feature_img = sitk.GradientMagnitude(img)
myshow(feature_img, "Edge Features")



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ws_img = sitk.MorphologicalWatershed(feature_img, level=0, markWatershedLine=True, fullyConnected=False)
myshow(sitk.LabelToRGB(ws_img), "Watershed Over Segmentation")



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from ipywidgets import interact, interactive, FloatSlider

def callback(feature_img,*args, **kwargs):
    ws_img = sitk.MorphologicalWatershed(feature_img,*args, **kwargs)
    
    myshow(sitk.LabelToRGB(ws_img), "Watershed Segmentation")
    
interact(lambda **kwargs: callback(feature_img, **kwargs),
             markWatershedLine=True,
             fullyConnected=False,
             level=FloatSlider(min=0, max=255, step=0.1, value=4.0) )

Segmentation From Markers



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min_img = sitk.RegionalMinima(feature_img, backgroundValue=0, foregroundValue=1.0, fullyConnected=False, flatIsMinima=True)
marker_img = sitk.ConnectedComponent(min_img, fullyConnected=False)
myshow(sitk.LabelToRGB(marker_img), "Too many local minima markers")



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ws = sitk.MorphologicalWatershedFromMarkers(feature_img, marker_img, markWatershedLine=True, fullyConnected=False)
myshow(sitk.LabelToRGB(ws), "Watershed Oversegmentation from markers")



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pt = [60,60]
idx = img.TransformPhysicalPointToIndex(pt)
marker_img *= 0
marker_img[0,0] = 1
marker_img[idx] = 2

ws = sitk.MorphologicalWatershedFromMarkers(feature_img, marker_img, markWatershedLine=True, fullyConnected=False)
myshow(sitk.LabelOverlay(img, ws, opacity=.2), "Watershed Oversegmentation from manual markers")

Binary Watersheds for Object Separation



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rgb_img = sitk.ReadImage(fdata("coins.png"))
myshow(rgb_img, "coins.png")
img = sitk.VectorIndexSelectionCast(rgb_img,1)
myshow(img, "Green Coins")



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feature_img = sitk.GradientMagnitudeRecursiveGaussian(img, sigma=1.5)
myshow(feature_img)



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ws_img = sitk.MorphologicalWatershed(feature_img, level=4, markWatershedLine=False, fullyConnected=False)
myshow(sitk.LabelToRGB(ws_img), "Watershed Over Segmentation")
seg = sitk.ConnectedComponent(ws_img!=ws_img[0,0])
myshow(sitk.LabelOverlay(img, seg), "Foreground Components")



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filled = sitk.BinaryFillhole(seg!=0)
d = sitk.SignedMaurerDistanceMap(filled, insideIsPositive=False, squaredDistance=False, useImageSpacing=False)
myshow(d, "Inside Distance Map")



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ws = sitk.MorphologicalWatershed( d, markWatershedLine=False, level=1)
myshow(sitk.LabelOverlay(img, ws))



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ws = sitk.Mask( ws, sitk.Cast(seg, ws.GetPixelID()))
myshow(sitk.LabelOverlay(img, ws), "Split Objects")

Multi-label Morphology



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seg = ws



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radius=10
bd_img = sitk.BinaryDilate(seg!=0, radius)
myshow(bd_img, "Binary Dilate")



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dist_img = sitk.SignedMaurerDistanceMap(seg!=0, insideIsPositive=False, squaredDistance=False, useImageSpacing=False)
wsd_img = sitk.MorphologicalWatershedFromMarkers(dist_img, seg, markWatershedLine=False)
myshow(sitk.LabelOverlay(img,wsd_img))



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md_img = sitk.Mask(wsd_img,bd_img)
myshow(sitk.LabelToRGB(md_img), "Multi-label Dilate")



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e_img=sitk.BinaryErode(md_img!=0, radius)
mo_img=sitk.Mask(md_img, e_img)
myshow(sitk.LabelOverlay(img, mo_img), "Multi-label Closing")



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