def adaptive_thres_two(img, FIL1=10, FIL2=100, R1=100, R2=100):
"""adaptive thresholding for picking objects with different brightness.
FIL2 and R2 for removing background.
"""
bw = adaptive_thresh(img, R=R1, FILTERINGSIZE=FIL1)
foreground = adaptive_thresh(img, R=R2, FILTERINGSIZE=FIL2)
bw[-foreground] = 0
return label(bw)
def adaptive_thres_otsu(img, FIL1=4, R1=1):
"""adaptive thresholding for picking objects with different brightness.
Use Otsu's method for removing background
"""
bw = adaptive_thresh(img, R1, FIL1)
foreground = global_otsu(img) > 0
bw[-foreground] = 0
return label(bw)
def n4_illum_correction(img, RATIO=1.5, FILTERINGSIZE=50):
"""
Implementation of the N4 bias field correction algorithm.
Takes some calculation time. It first calculates the background using adaptive_thesh.
"""
bw = adaptive_thresh(img, R=RATIO, FILTERINGSIZE=FILTERINGSIZE)
img = homogenize_intensity_n4(img, -bw)
return img
def ring_dilation_above_adaptive(labels,
img,
MARGIN=0,
RINGWIDTH=4,
BUFFER=2,
RATIO=1.05,
FILSIZE=10):
sub_labels = dilate_to_cytoring_buffer(labels, RINGWIDTH, MARGIN, BUFFER)
bw = adaptive_thresh(img, R=RATIO, FILTERINGSIZE=FILSIZE)
sub_labels[-bw] = 0
return sub_labels
def n4_illum_correction_downsample(img, 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, R=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)
def agglomeration_seed(labels, img, MINSIZE=50, STEPS=100, FILSIZE=5, RATIO=0):
"""
MINSIZE: minimum area for a seed object. It can be smaller than actual objects.
STEPS: Larger it is, more resolution and computation
FILSIZE: argument for adaptive thresholding. Larger if capturing too much backrgound.
RATIO: argument for adaptive thresholding. Larger if capturing too much backrgound.
"""
seed = binary_erosion(labels, np.ones((3, 3)))
li = []
img = img.astype(np.float32)
mask = adaptive_thresh(img, RATIO, FILSIZE)
mask = binary_opening(mask, np.ones((3, 3)))
mask = remove_small_objects(mask, MINSIZE)
foreground = img[mask]
perclist = [
np.percentile(foreground, r) for r in np.linspace(0, 100, STEPS)
]
for _r in perclist:
thresed = remove_small_objects(img > _r, MINSIZE, connectivity=2) > 0
li.append(thresed.astype(np.uint16))
if seed is not None:
li.append((seed > 0).astype(np.uint16))
for l in li:
l[seed > 0] = 1
q = np.sum(np.dstack(li), axis=2)
p = label(q)
for ind in reversed(np.unique(q).tolist()):
c = seeding_separate(label(q >= ind), p)
w = watershed(q >= ind,
markers=c,
mask=(q >= ind),
watershed_line=True)
w[mask == 0] = 0
w = remove_small_objects(w, MINSIZE)
p = label(w, connectivity=2)
return p
def adaptive_thres(img, FIL1=10, R1=100):
"""adaptive thresholding for picking objects with different brightness.
"""
bw = adaptive_thresh(img, R=R1, FILTERINGSIZE=FIL1)
return label(bw)
