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https://github.com/wassname/scikit-image.git
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emmanuelle
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
"""
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==========================
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Random walker segmentation
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==========================
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The random walker algorithm (*Random walks for image segmentation*, Leo Grady,
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IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83) determines the
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segmentation of an image from a set of markers labeling several phases (2 or
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more). An anisotropic diffusion equation is solved with tracers initiated
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at the markers' position. The local diffusivity coefficient is greater if
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neighboring pixels have similar values, so that diffusion is difficult across
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high gradients. The label of each unknown pixel is attributed to the label
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of the known marker that has the highest probability to be reached first
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during this diffusion process.
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In this example, two phases are clearly visible, but the data are too
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noisy to perform the segmentation from the histogram only. We determine
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markers of the two phases from the extreme tails of the histogram of gray
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values, and use the random walker for the segmentation.
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"""
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print __doc__
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import numpy as np
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from scipy import ndimage
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from skimage.segmentation import random_walker
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import matplotlib.pyplot as plt
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def microstructure(l=256):
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"""
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Synthetic binary data: binary microstructure with blobs.
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Parameters
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----------
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l: int, optional
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linear size of the returned image
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"""
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n = 5
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x, y = np.ogrid[0:l, 0:l]
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mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2
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mask = np.zeros((l, l))
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generator = np.random.RandomState(1)
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points = l * generator.rand(2, n ** 2)
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mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
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mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
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return (mask > mask.mean()).astype(np.float)
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# Generate noisy synthetic data
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data = microstructure(l=128)
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data += 0.35 * np.random.randn(*data.shape)
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markers = np.zeros(data.shape, dtype=np.uint)
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markers[data < -0.3] = 1
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markers[data > 1.3] = 2
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# Run random walker algorithm
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labels = random_walker(data, markers, beta=10, mode='bf')
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# Plot results
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plt.figure(figsize=(9, 3.5))
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plt.subplot(131)
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plt.imshow(data, cmap='gray', interpolation='nearest')
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plt.axis('off')
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plt.title('Noisy data')
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plt.subplot(132)
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plt.imshow(markers, cmap='hot', interpolation='nearest')
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plt.axis('off')
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plt.title('Markers')
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plt.subplot(133)
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plt.imshow(labels, cmap='gray', interpolation='nearest')
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plt.axis('off')
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plt.title('Segmentation')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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plt.show()
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