def find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue): # FIND PEAKS # sigmaSmaller ==> Size of the smaller dots (in pixels) # sigmaLarger ==> Size of the bigger dots (in pixels) # minPeakValue ==> Intensity above which to look for dots # Preparation Neuron channel ip1_1 = IL.wrapReal(imp1) ip1E = Views.extendMirrorSingle(ip1_1) imp1.show() #Preparation Glioma channel ip2_1 = IL.wrapReal(imp2) ip2E = Views.extendMirrorSingle(ip2_1) imp2.show() calibration = [1.0 for i in range(ip1_1.numDimensions())] extremaType = DogDetection.ExtremaType.MINIMA normalizedMinPeakValue = False dog_1 = DogDetection(ip1E, ip1_1, calibration, sigmaSmaller, sigmaLarger, extremaType, minPeakValue, normalizedMinPeakValue) dog_2 = DogDetection(ip2E, ip2_1, calibration, sigmaSmaller, sigmaLarger, extremaType, minPeakValue, normalizedMinPeakValue) peaks_1 = dog_1.getPeaks() peaks_2 = dog_2.getPeaks() return ip1_1, ip2_1, peaks_1, peaks_2
def dog_detection(overlay,img, imp, cal):
# Create a variable of the correct type (UnsignedByteType) for the value-extended view
zero = img.randomAccess().get().createVariable()
# Run the difference of Gaussian
cell = 8.0 # microns in diameter
min_peak = 2.0 # min intensity for a peak to be considered
dog = DogDetection(Views.extendValue(img, zero), img,
[cal.pixelWidth, cal.pixelHeight,cal.pixelDepth],
cell / 2, cell,
DogDetection.ExtremaType.MINIMA,
min_peak, False,
DoubleType())
peaks = dog.getPeaks()
roi = OvalRoi(0, 0, cell/cal.pixelWidth, cell/cal.pixelHeight)
print ('Number of cells = ', len(peaks))
p = zeros(img.numDimensions(), 'i')
boundRect = imp.getRoi()
for peak in peaks:
# Read peak coordinates into an array of integers XYZ location of spots
peak.localize(p)
print(p)
if(boundRect is not None and boundRect.contains(p[0], p[1])):
oval = OvalRoi(p[0], p[1],cell/cal.pixelWidth, cell/cal.pixelHeight)
oval.setColor(Color.RED)
overlay.add(oval)
# Parameters for a Difference of Gaussian to detect embryo positions
calibration = [1.0
for i in range(img.numDimensions())] # no calibration: identity
sigmaSmaller = 15 # in pixels: a quarter of the radius of an embryo
sigmaLarger = 30 # pixels: half the radius of an embryo
extremaType = DogDetection.ExtremaType.MAXIMA
minPeakValue = 10
normalizedMinPeakValue = False
# In the differece of gaussian peak detection, the img acts as the interval
# within which to look for peaks. The processing is done on the infinite imgE.
dog = DogDetection(imgE, img, calibration, sigmaSmaller, sigmaLarger,
extremaType, minPeakValue, normalizedMinPeakValue)
peaks = dog.getPeaks()
# Create a PointRoi from the DoG peaks, for visualization
roi = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p = zeros(img.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks:
# Read peak coordinates into an array of integers
peak.localize(p)
roi.addPoint(imp, p[0], p[1])
imp.setRoi(roi)
# Now, iterate each peak, defining a small interval centered at each peak,
# and measure the sum of total pixel intensity,