def background_subtraction_wavelet(img, holder, level=7, OFFSET=10):
'''
It might be radical but works in many cases in terms of segmentation.
Use "background_subtraction_wavelet_hazen" for a proper implementation.
'''
img = wavelet_subtraction(img, level)
return convert_positive(img, OFFSET)
def background_subtraction_prcblock(img,
holder,
BLOCK=10,
OPEN=3,
PERCENTILE=0.1,
OFFSET=50):
img = median_filter(img, size=(5, 5))
img = estimate_background_prc(img, BLOCK, PERCENTILE)
img = convert_positive(img, OFFSET)
return img
def background_subtraction_wavelet_hazen(img,
holder,
THRES=100,
ITER=5,
WLEVEL=6,
OFFSET=50):
"""Wavelet background subtraction for STORM.
"""
back = wavelet_subtraction_hazen(img,
ITER=ITER,
THRES=THRES,
WLEVEL=WLEVEL)
img = img - back
return convert_positive(img, OFFSET)
def n4_illum_correction_downsample(img,
holder,
DOWN=2,
RATIO=1.05,
FILTERINGSIZE=50,
OFFSET=10):
"""Faster but more insensitive to local illum bias.
"""
fil = sitk.ShrinkImageFilter()
cc = sitk.GetArrayFromImage(
fil.Execute(sitk.GetImageFromArray(img), [DOWN, DOWN]))
bw = adaptive_thresh(cc, RATIO=RATIO, FILTERINGSIZE=FILTERINGSIZE / DOWN)
himg = homogenize_intensity_n4(cc, -bw)
himg = cc - himg
# himg[himg < 0] = 0
bias = resize_img(himg, img.shape)
img = img - bias
return convert_positive(img, OFFSET)