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"""
===================================
Brain segmentation with median_otsu
===================================
We show how to extract brain information and mask from a b0 image using DIPY_'s
``segment.mask`` module.
First import the necessary modules:
"""
import matplotlib.pyplot as plt
import numpy as np
from dipy.core.histeq import histeq
from dipy.data import get_fnames
from dipy.io.image import load_nifti, save_nifti
from dipy.segment.mask import median_otsu
###############################################################################
# Download and read the data for this tutorial.
#
# The ``scil_b0`` dataset contains different data from different companies and
# models. For this example, the data comes from a 1.5 Tesla Siemens MRI.
data_fnames = get_fnames(name="scil_b0")
data, affine = load_nifti(data_fnames[1])
data = np.squeeze(data)
###############################################################################
# Segment the brain using DIPY's ``mask`` module.
#
# ``median_otsu`` returns the segmented brain data and a binary mask of the
# brain. It is possible to fine tune the parameters of ``median_otsu``
# (``median_radius`` and ``num_pass``) if extraction yields incorrect results
# but the default parameters work well on most volumes. For this example,
# we used 2 as ``median_radius`` and 1 as ``num_pass``
b0_mask, mask = median_otsu(data, median_radius=2, numpass=1)
###############################################################################
# Saving the segmentation results is very easy. We need the ``b0_mask``, and
# the binary mask volumes. The affine matrix which transform the image's
# coordinates to the world coordinates is also needed. Here, we choose to save
# both images in ``float32``.
fname = "se_1.5t"
save_nifti(fname + "_binary_mask.nii.gz", mask.astype(np.float32), affine)
save_nifti(fname + "_mask.nii.gz", b0_mask.astype(np.float32), affine)
###############################################################################
# Quick view of the results middle slice using ``matplotlib``.
sli = data.shape[2] // 2
plt.figure("Brain segmentation")
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(histeq(data[:, :, sli].astype("float")).T, cmap="gray", origin="lower")
plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(histeq(b0_mask[:, :, sli].astype("float")).T, cmap="gray", origin="lower")
plt.savefig(f"{fname}_median_otsu.png", bbox_inches="tight")
###############################################################################
# .. rst-class:: centered small fst-italic fw-semibold
#
# An application of median_otsu for brain segmentation.
#
#
# ``median_otsu`` can also automatically crop the outputs to remove the largest
# possible number of background voxels. This makes outputted data significantly
# smaller. Auto-cropping in ``median_otsu`` is activated by setting the
# ``autocrop`` parameter to ``True``.
b0_mask_crop, mask_crop = median_otsu(data, median_radius=4, numpass=4, autocrop=True)
###############################################################################
# Saving cropped data as demonstrated previously.
save_nifti(fname + "_binary_mask_crop.nii.gz", mask_crop.astype(np.float32), affine)
save_nifti(fname + "_mask_crop.nii.gz", b0_mask_crop.astype(np.float32), affine)
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