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    <p>I am assuming that you have an image that shows some kind of peak, and you are interesting in getting the Skewness and Kurtosis (and probably the standard deviation and centroid) of that peak in both the x and y directions. </p> <p>I was wondering about this as well. Curiously, I did not find this implemented into any of the python image analysis packages. OpenCV has a <a href="http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=moments#moments" rel="nofollow noreferrer">moments function</a>, and we should be able to get the skewness from these, but the moments only go to 3rd order, and we need 4th order to get kurtosis. </p> <p>To make things easier and faster, I think that taking the projection of the image in the x and y directions and finding the statistics from these projections is mathematically equivalent to finding the statistics using the full image. In the following code, I use both methods and show that they are the same for this smooth example. Using a real, noisy image, I found that the two methods also provide the same result, but only if you manually cast the image data to float64 (it imported as float 32, and "numerical stuff" caused the results to be slightly different. </p> <p>Below is an example. You should be able to cut and paste the "image_statistics()" function into your own code. Hopefully it works for someone! :)</p> <pre><code>import numpy as np import matplotlib.pyplot as plt import time plt.figure(figsize=(10,10)) ax1 = plt.subplot(221) ax2 = plt.subplot(222) ax4 = plt.subplot(224) #Make some sample data as a sum of two elliptical gaussians: x = range(200) y = range(200) X,Y = np.meshgrid(x,y) def twoD_gaussian(X,Y,A=1,xo=100,yo=100,sx=20,sy=10): return A*np.exp(-(X-xo)**2/(2.*sx**2)-(Y-yo)**2/(2.*sy**2)) Z = twoD_gaussian(X,Y) + twoD_gaussian(X,Y,A=0.4,yo=75) ax2.imshow(Z) #plot it #calculate projections along the x and y axes for the plots yp = np.sum(Z,axis=1) xp = np.sum(Z,axis=0) ax1.plot(yp,np.linspace(0,len(yp),len(yp))) ax4.plot(np.linspace(0,len(xp),len(xp)),xp) #Here is the business: def image_statistics(Z): #Input: Z, a 2D array, hopefully containing some sort of peak #Output: cx,cy,sx,sy,skx,sky,kx,ky #cx and cy are the coordinates of the centroid #sx and sy are the stardard deviation in the x and y directions #skx and sky are the skewness in the x and y directions #kx and ky are the Kurtosis in the x and y directions #Note: this is not the excess kurtosis. For a normal distribution #you expect the kurtosis will be 3.0. Just subtract 3 to get the #excess kurtosis. import numpy as np h,w = np.shape(Z) x = range(w) y = range(h) #calculate projections along the x and y axes yp = np.sum(Z,axis=1) xp = np.sum(Z,axis=0) #centroid cx = np.sum(x*xp)/np.sum(xp) cy = np.sum(y*yp)/np.sum(yp) #standard deviation x2 = (x-cx)**2 y2 = (y-cy)**2 sx = np.sqrt( np.sum(x2*xp)/np.sum(xp) ) sy = np.sqrt( np.sum(y2*yp)/np.sum(yp) ) #skewness x3 = (x-cx)**3 y3 = (y-cy)**3 skx = np.sum(xp*x3)/(np.sum(xp) * sx**3) sky = np.sum(yp*y3)/(np.sum(yp) * sy**3) #Kurtosis x4 = (x-cx)**4 y4 = (y-cy)**4 kx = np.sum(xp*x4)/(np.sum(xp) * sx**4) ky = np.sum(yp*y4)/(np.sum(yp) * sy**4) return cx,cy,sx,sy,skx,sky,kx,ky #We can check that the result is the same if we use the full 2D data array def image_statistics_2D(Z): h,w = np.shape(Z) x = range(w) y = range(h) X,Y = np.meshgrid(x,y) #Centroid (mean) cx = np.sum(Z*X)/np.sum(Z) cy = np.sum(Z*Y)/np.sum(Z) ###Standard deviation x2 = (range(w) - cx)**2 y2 = (range(h) - cy)**2 X2,Y2 = np.meshgrid(x2,y2) #Find the variance vx = np.sum(Z*X2)/np.sum(Z) vy = np.sum(Z*Y2)/np.sum(Z) #SD is the sqrt of the variance sx,sy = np.sqrt(vx),np.sqrt(vy) ###Skewness x3 = (range(w) - cx)**3 y3 = (range(h) - cy)**3 X3,Y3 = np.meshgrid(x3,y3) #Find the thid central moment m3x = np.sum(Z*X3)/np.sum(Z) m3y = np.sum(Z*Y3)/np.sum(Z) #Skewness is the third central moment divided by SD cubed skx = m3x/sx**3 sky = m3y/sy**3 ###Kurtosis x4 = (range(w) - cx)**4 y4 = (range(h) - cy)**4 X4,Y4 = np.meshgrid(x4,y4) #Find the fourth central moment m4x = np.sum(Z*X4)/np.sum(Z) m4y = np.sum(Z*Y4)/np.sum(Z) #Kurtosis is the fourth central moment divided by SD to the fourth power kx = m4x/sx**4 ky = m4y/sy**4 return cx,cy,sx,sy,skx,sky,kx,ky #Calculate the image statistics using the projection method stats_pr = image_statistics(Z) #Confirm that they are the same by using a 2D calculation stats_2d = image_statistics_2D(Z) names = ('Centroid x','Centroid y','StdDev x','StdDev y','Skewness x','Skewness y','Kurtosis x','Kurtosis y') print 'Statistis\t1D\t2D' for name,i1,i2 in zip(names, stats_2d,stats_pr): print '%s \t%.2f \t%.2f'%(name, i1,i2) plt.show() </code></pre> <p>Screen shot of output, just for fun:</p> <p><img src="https://i.stack.imgur.com/7o7Z3.png" alt="screen shot of output"></p> <p>One more thing: depending on exactly what you are doing with the images, you might consider using <a href="http://rsb.info.nih.gov/ij/" rel="nofollow noreferrer">ImageJ</a> for your image analysis – but beware! The <a href="http://rsb.info.nih.gov/ij/plugins/moments.html" rel="nofollow noreferrer">moments plugin</a> will let you calculate the skewness, kurtosis, etc. ImageJ does have a "skewness" and "kurtosis" in Analyze>>Set Measurements menu, but I think that this actually finds the skewness and kurtosis of the intensity histogram (I was fooled for a minute). </p>
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