def find_spinal_cord(y):
y_spine = get_mask_full_spine(y)
# зальем "по y"
y_ = apply_along_axes(fill_line, y_spine, axes=(1, 2)).astype(bool)
# возьмем неплотное внутри
y_n = (y < 100) & y_
# свернем с цилиндром 5 default
mask = np.stack([disk(5) for i in range(20)])
y__ = convolve(y_n.astype(int), mask) / mask.sum()
y_f = get_greatest_component(y__ > 0.85)
return y_f
def limits_to_mask(limits, threshold=.3, width_ratio=.7):
mask = limits >= threshold
mask = get_greatest_component(mask)
# take convex hull line by line in order to fill "holes" inside the brain
limits_mask = apply_along_axes(
lambda s: convex_hull_image(s[None])[0] if s.any() else s, mask, -1)
widths = mask.sum(0)
# drop the lines that are too thin - these are just outliers
start, stop = mask2bounding_box(
widths >= width_ratio * widths.max()).flatten()
limits_mask[:, :start] = 0
return limits_mask.astype(bool)
def test_apply(self):
x = np.random.rand(3, 10, 10) * 2 + 3
np.testing.assert_array_almost_equal(
apply_along_axes(normalize, x, axes=(1, 2), percentiles=20),
normalize(x, percentiles=20, axes=0)
)
axes = (0, 2)
y = apply_along_axes(min_max_scale, x, axes)
np.testing.assert_array_almost_equal(y.max(axes), 1)
np.testing.assert_array_almost_equal(y.min(axes), 0)
np.testing.assert_array_almost_equal(apply_along_axes(identity, x, 1), x)
np.testing.assert_array_almost_equal(apply_along_axes(identity, x, -1), x)
np.testing.assert_array_almost_equal(apply_along_axes(identity, x, (0, 1)), x)
np.testing.assert_array_almost_equal(apply_along_axes(identity, x, (0, 2)), x)
def remove_background(x):
# otsu -> greatest connected component -> convex hull
mask = get_greatest_component(x > threshold_otsu(x))
mask = apply_along_axes(lambda s: convex_hull_image(s)
if s.any() else s, mask, (0, 1))
return x * mask