.. AUTO-GENERATED FILE -- DO NOT EDIT! .. _example_register_binary_fuzzy: ========================================================= Diffeomorphic Registration with binary and fuzzy images ========================================================= This example demonstrates registration of a binary and a fuzzy image. This could be seen as aligning a fuzzy (sensed) image to a binary (e.g., template) image. :: import numpy as np import matplotlib.pyplot as plt from skimage import draw, filters from dipy.align.imwarp import SymmetricDiffeomorphicRegistration from dipy.align.metrics import SSDMetric from dipy.viz import regtools Let's generate a sample template image as the combination of three ellipses. We will generate the fuzzy (sensed) version of the image by smoothing the reference image. :: def draw_ellipse(img, center, axis): rr, cc = draw.ellipse(center[0], center[1], axis[0], axis[1], shape=img.shape) img[rr, cc] = 1 return img img_ref = np.zeros((64, 64)) img_ref = draw_ellipse(img_ref, (25, 15), (10, 5)) img_ref = draw_ellipse(img_ref, (20, 45), (15, 10)) img_ref = draw_ellipse(img_ref, (50, 40), (7, 15)) img_in = filters.gaussian(img_ref, sigma=3) Let's define a small visualization function. :: def show_images(img_ref, img_warp, fig_name): fig, axarr = plt.subplots(ncols=2, figsize=(12, 5)) axarr[0].set_title('warped image & reference contour') axarr[0].imshow(img_warp) axarr[0].contour(img_ref, colors='r') ssd = np.sum((img_warp - img_ref) ** 2) axarr[1].set_title('difference, SSD=%.02f' % ssd) im = axarr[1].imshow(img_warp - img_ref) plt.colorbar(im) fig.tight_layout() fig.savefig(fig_name + '.png') show_images(img_ref, img_in, 'input') .. figure:: input.png :align: center Input images before alignment. :: Let's the use the general Registration function with some naive parameters, such as set `step_length` as 1 assuming maximal step 1 pixel and reasonable small number of iteration since the deformation with already aligned images should be minimal. :: sdr = SymmetricDiffeomorphicRegistration(metric=SSDMetric(img_ref.ndim), step_length=1.0, level_iters=[50, 100], inv_iter=50, ss_sigma_factor=0.1, opt_tol=1.e-3) Perform the registration with equal images. :: mapping = sdr.optimize(img_ref.astype(float), img_ref.astype(float)) img_warp = mapping.transform(img_ref, 'linear') show_images(img_ref, img_warp, 'output-0') regtools.plot_2d_diffeomorphic_map(mapping, 5, 'map-0.png') .. figure:: output-0.png :align: center .. figure:: map-0.png :align: center Registration results for default parameters and equal images. :: Perform the registration with binary and fuzzy images. :: mapping = sdr.optimize(img_ref.astype(float), img_in.astype(float)) img_warp = mapping.transform(img_in, 'linear') show_images(img_ref, img_warp, 'output-1') regtools.plot_2d_diffeomorphic_map(mapping, 5, 'map-1.png') .. figure:: output-1.png :align: center .. figure:: map-1.png :align: center Registration results for a naive parameter configuration. :: Note, we are still using multi-scale approach which makes `step_length` in the upper level multiplicatively larger. What happens if we set `step_length` to a rather small value? :: sdr.step_length = 0.1 Perform the registration and examine the output. :: mapping = sdr.optimize(img_ref.astype(float), img_in.astype(float)) img_warp = mapping.transform(img_in, 'linear') show_images(img_ref, img_warp, 'output-2') regtools.plot_2d_diffeomorphic_map(mapping, 5, 'map-2.png') .. figure:: output-2.png :align: center .. figure:: map-2.png :align: center Registration results for decreased step size. :: An alternative scenario is to use just a single scale level. Even though the warped image may look fine, the estimated deformations show that it is off the mark. :: sdr = SymmetricDiffeomorphicRegistration(metric=SSDMetric(img_ref.ndim), step_length=1.0, level_iters=[100], inv_iter=50, ss_sigma_factor=0.1, opt_tol=1.e-3) Perform the registration. :: mapping = sdr.optimize(img_ref.astype(float), img_in.astype(float)) img_warp = mapping.transform(img_in, 'linear') show_images(img_ref, img_warp, 'output-3') regtools.plot_2d_diffeomorphic_map(mapping, 5, 'map-3.png') .. figure:: output-3.png :align: center .. figure:: map-3.png :align: center Registration results for single level. .. admonition:: Example source code You can download :download:`the full source code of this example <./register_binary_fuzzy.py>`. This same script is also included in the dipy source distribution under the :file:`doc/examples/` directory.