79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
"""
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===============
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Ridge operators
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===============
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Ridge filters can be used to detect ridge-like structures, such as neurites
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[1]_, tubes [2]_, vessels [3]_, wrinkles [4]_ or rivers.
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Different ridge filters may be suited for detecting different structures,
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e.g., depending on contrast or noise level.
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The present class of ridge filters relies on the eigenvalues of
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the Hessian matrix of image intensities to detect ridge structures where the
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intensity changes perpendicular but not along the structure.
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Note that, due to edge effects, results for Meijering and Frangi filters
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are cropped by 4 pixels on each edge to get a proper rendering.
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References
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----------
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.. [1] Meijering, E., Jacob, M., Sarria, J. C., Steiner, P., Hirling, H.,
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Unser, M. (2004). Design and validation of a tool for neurite tracing
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and analysis in fluorescence microscopy images. Cytometry Part A, 58(2),
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167-176.
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:DOI:`10.1002/cyto.a.20022`
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.. [2] Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S.,
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Koller, T., ..., Kikinis, R. (1998). Three-dimensional multi-scale line
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filter for segmentation and visualization of curvilinear structures in
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medical images. Medical image analysis, 2(2), 143-168.
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:DOI:`10.1016/S1361-8415(98)80009-1`
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.. [3] Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998,
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October). Multiscale vessel enhancement filtering. In International
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Conference on Medical Image Computing and Computer-Assisted Intervention
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(pp. 130-137). Springer Berlin Heidelberg.
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:DOI:`10.1007/BFb0056195`
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.. [4] Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2014, November). Automatic
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wrinkle detection using hybrid Hessian filter. In Asian Conference on
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Computer Vision (pp. 609-622). Springer International Publishing.
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:DOI:`10.1007/978-3-319-16811-1_40`
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"""
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from skimage import data
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from skimage import color
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from skimage.filters import meijering, sato, frangi, hessian
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import matplotlib.pyplot as plt
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def identity(image, **kwargs):
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"""Return the original image, ignoring any kwargs."""
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return image
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image = color.rgb2gray(data.retina())[300:700, 700:900]
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cmap = plt.cm.gray
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kwargs = {'sigmas': [1], 'mode': 'reflect'}
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fig, axes = plt.subplots(2, 5)
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for i, black_ridges in enumerate([1, 0]):
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for j, func in enumerate([identity, meijering, sato, frangi, hessian]):
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kwargs['black_ridges'] = black_ridges
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result = func(image, **kwargs)
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axes[i, j].imshow(result, cmap=cmap, aspect='auto')
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if i == 0:
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axes[i, j].set_title(['Original\nimage', 'Meijering\nneuriteness',
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'Sato\ntubeness', 'Frangi\nvesselness',
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'Hessian\nvesselness'][j])
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if j == 0:
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axes[i, j].set_ylabel('black_ridges = ' + str(bool(black_ridges)))
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axes[i, j].set_xticks([])
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axes[i, j].set_yticks([])
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plt.tight_layout()
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plt.show()
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