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Function normgausker normalization

Synopse

The normgausker function performs a local normalization algorithm based on the Gaussian Kernel

  • g = *normgausker(f)
    • Output
      • g: output image
    • Input
      • f: ndarray: input image.

Description

The normgausker function performs a local normalization algorithm used to normalize the local mean and variance of the image estimated by a Gaussian Kernel using smoothing operators

Function Code

1 def normgausker2(f):
2     mu = f.mean()
3     sigma = f.std()
4     return 1.0*(f-mu)/sigma

Example

 1 import ia636
 2 from normgausker import normgausker
 3 
 4 f = adreadgray('p/LesionMRI/EM/Iani Surian Batalini - 746587-5 - 32A/22.png')
 5 roi = adreadgray('p/LesionMRI/EM/Iani Surian Batalini - 746587-5 - 32A/Peri_22_1.png')>0
 6 adshow(f, 'original image')
 7 print 'f.min,f.max()',f.min(),f.max()
 8 
 9 result = normgausker(f)
10 adshow(ia636.ianormalize(result),'norm gaussian kernel result')
11 print 'result.min,result.max()',result.min(),result.max()
f.min,f.max() 1 254
result.min,result.max() -0.704345758699 6.58994984826

original image

norm gaussian kernel result