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get_fdata
was added in version 2.2? Are you using an older version? If so, try upgrading.
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I have dicom files to auto segment using atlas and CNN approach. I’m looking to measure correspondence between these automatic contours and manual contours created by experts. Kindly advise what techniques are best
-Hausdoff distance
-structural similarity index (SSIM)
-Bidirectional local distance measurement (BLD)
-Mean square errors (MSE)
-Dice similarity coefficient
-Distance transforms
Please advise if there are any I haven’t listed and which ones are best for the operation I want to carry out.
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from matplotlib import pyplot as plt
from skimage.draw import disk
fig, axes = plt.subplots(1, 2)
for ax, shape in zip(axes, [None, (10, 10)]):
img = np.zeros((10, 10), dtype=np.uint8)
rr, cc = disk((1, 1), 5, shape=shape)
img[rr, cc] = 1
ax.imshow(img)
(8,8)
give me a partial part of it?
from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
from skimage import filters
from skimage import io
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
image = io.imread('C:/Users/DELL/Desktop/hopfield/monaliza.jpg')
try_to_apply_sobel = filters.sobel(image)
plt.imshow(try_to_apply_sobel)
image
must be a 2-dimensional array
image.shape
. Or are you suggesting that the error message be made more clear? I encourage you to join the official chatroom at https://skimage.zulipchat.com to propose something, if so.