Mri example

Viewing MRI data with cut plane and iso surface.

This example downloads an MRI scan, turns it into a 3D numpy array and visualizes it.

Mayavi User Guide, Release 3.3.1

First we extract some internal structures of the brain by defining a volume of interest around them, and using iso surfaces.

Then we display two cut planes to show the raw MRI data itself.

Finally we display the outer surface, but we restrict it to volume of interest to leave a cut for the cut planes. For an example of feature extraction from MRI data using Mayavi and vtk, seeTvtk segmentation example.

Source code: mri.py

### Download the data, if not already on disk ################################## import os

if not os.path.exists(’mri_data.tar.gz’): # Download the data

import urllib

print "Downloading data, Please Wait (7.8MB)" opener = urllib.urlopen(

’http://www-graphics.stanford.edu/data/voldata/MRbrain.tar.gz’)

open(’mri_data.tar.gz’, ’wb’).write(opener.read()) # Extract the data

import tarfile

tar_file = tarfile.open(’mri_data.tar.gz’) try:

Mayavi User Guide, Release 3.3.1 os.mkdir(’mri_data’) except: pass tar_file.extractall(’mri_data’) tar_file.close()

### Read the data in a numpy 3D array ########################################## import numpy as np

data = np.array([np.fromfile(os.path.join(’mri_data’, ’MRbrain.%i’ % i), dtype=’>u2’) for i in range(1, 110)]) data.shape = (109, 256, 256)

data = data.T

# Display the data ############################################################# from enthought.mayavi import mlab

mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))

src = mlab.pipeline.scalar_field(data)

# Our data is not equally spaced in all directions: src.spacing = [1, 1, 1.5]

src.update_image_data = True

# Extract some inner structures: the ventricles and the inter-hemisphere # fibers. We define a volume of interest (VOI) that restricts the

# iso-surfaces to the inner of the brain. We do this with the ExtractGrid # filter.

blur = mlab.pipeline.user_defined(src, filter=’ImageGaussianSmooth’) voi = mlab.pipeline.extract_grid(blur)

voi.set(x_min=125, x_max=193, y_min=92, y_max=125, z_min=34, z_max=75)

mlab.pipeline.iso_surface(voi, contours=[1610, 2480], colormap=’Spectral’)

# Add two cut planes to show the raw MRI data. We use a threshold filter # to remove cut the planes outside the brain.

thr = mlab.pipeline.threshold(src, low=1120) cut_plane = mlab.pipeline.scalar_cut_plane(thr,

plane_orientation=’y_axes’, colormap=’black-white’, vmin=1400,

vmax=2600)

cut_plane.implicit_plane.origin = (136, 111.5, 82) cut_plane.implicit_plane.widget.enabled = False

cut_plane2 = mlab.pipeline.scalar_cut_plane(thr,

plane_orientation=’z_axes’, colormap=’black-white’, vmin=1400,

vmax=2600)

cut_plane2.implicit_plane.origin = (136, 111.5, 82) cut_plane2.implicit_plane.widget.enabled = False

# Extract two views of the outside surface. We need to define VOIs in # order to leave out a cut in the head.

voi2 = mlab.pipeline.extract_grid(src) voi2.set(y_min=112)

Mayavi User Guide, Release 3.3.1

outer = mlab.pipeline.iso_surface(voi2, contours=[1776, ], color=(0.8, 0.7, 0.6))

voi3 = mlab.pipeline.extract_grid(src) voi3.set(y_max=112, z_max=53)

outer3 = mlab.pipeline.iso_surface(voi3, contours=[1776, ], color=(0.8, 0.7, 0.6)) mlab.view(-125, 54, 326, (145.5, 138, 66.5)) mlab.roll(-175) mlab.show() import shutil shutil.rmtree(’mri_data’)

10.2.16 Protein example

Visualize a protein graph structure downloaded from the protein database in standard pdb format.

We parse the pdb file, but extract only a very small amount of information: the type of atoms, their positions, and the links between them.

Most of the complexity of this example comes from the code turning the PDB information into a list of 3D positions, with associated scalar and connection information.

We assign a scalar value for the atoms to differenciate the different types of atoms, but it does not correspond to the atomic mass. The size and the color of the atom on the visualization is therefore not chemicaly-significant.

The atoms are plotted using mlab.points3d, and connections between atoms are added to the dataset, and visualized using a surface module.

The graph is created by adding connection information to points. For this, each point is designated by its number (in the order of the array passed to mlab.points3d), and the connection array, made of pairs of these numbers, is constructed. There is some slightly tedious data manipulation to go from the named-node graph representation as stored in the pdb file, to the index-based connection pairs. A similar technique to plot the graph is done in theFlight graph example. Another example of graph plotting, showing a different technique to plot the graph, can be seen onDelaunay graph example.

To visualize the local atomic density, we use a gaussian splatter filter that builds a kernel density estimation of the continuous density field: each point is convoluted by a Gaussian kernel, and the sum of these Gaussians form the resulting density field. We visualize this field using volume rendering.

Reference for the pdb file standard:http://mmcif.pdb.org/dictionaries/pdb-correspondence/pdb2mmcif.html

Mayavi User Guide, Release 3.3.1

Source code: protein.py

# Author: Gael Varoquaux <[email protected]> # Copyright (c) 2008, Enthought, Inc.

# License: BSD Style.

# The pdb code for the protein. protein_code = ’2q09’

# Retrieve the file from the protein database ################################## import os

if not os.path.exists(’pdb%s.ent.gz’ % protein_code): # Download the data

import urllib

print ’Downloading protein data, please wait’ opener = urllib.urlopen(

’ftp://ftp.wwpdb.org/pub/pdb/data/structures/divided/pdb/q0/pdb%s.ent.gz’

% protein_code)

open(’pdb%s.ent.gz’ % protein_code, ’w’).write(opener.read())

# Parse the pdb file ########################################################### import gzip

infile = gzip.GzipFile(’pdb%s.ent.gz’ % protein_code, ’rb’)

# A graph represented by a dictionary associating nodes with keys # (numbers), and edges (pairs of node keys).

nodes = dict() edges = list()

Mayavi User Guide, Release 3.3.1

atoms = set()

# Build the graph from the PDB information last_atom_label = None

last_chain_label = None

for line in infile: line = line.split()

if line[0] in (’ATOM’, ’HETATM’):

nodes[line[1]] = (line[2], line[6], line[7], line[8]) atoms.add(line[2])

chain_label = line[5]

if chain_label == last_chain_label:

edges.append((line[1], last_atom_label)) last_atom_label = line[1]

last_chain_label = chain_label elif line[0] == ’CONECT’:

for start, stop in zip(line[1:-1], line[2:]): edges.append((start, stop))

atoms = list(atoms) atoms.sort()

atoms = dict(zip(atoms, range(len(atoms))))

# Turn the graph into 3D positions, and a connection list. labels = dict()

x = list() y = list() z = list() scalars = list()

for index, label in enumerate(nodes): labels[label] = index

this_scalar, this_x, this_y, this_z= nodes[label] scalars.append(atoms[this_scalar])

x.append(float(this_x)) y.append(float(this_y)) z.append(float(this_z)) connections = list()

for start, stop in edges:

connections.append((labels[start], labels[stop]))

import numpy as np x = np.array(x) y = np.array(y) z = np.array(z) scalars = np.array(scalars)

# Visualize the data ########################################################### from enthought.mayavi import mlab

mlab.figure(1, bgcolor=(0, 0, 0)) mlab.clf()

pts = mlab.points3d(x, y, z, 1.5*scalars.max() - scalars,

scale_factor=0.015, resolution=10) pts.mlab_source.dataset.lines = np.array(connections)

Mayavi User Guide, Release 3.3.1

# Use a tube fiter to plot tubes on the link, varying the radius with the # scalar value

tube = mlab.pipeline.tube(pts, tube_radius=0.15) tube.filter.radius_factor = 1.

tube.filter.vary_radius = ’vary_radius_by_scalar’ mlab.pipeline.surface(tube, color=(0.8, 0.8, 0))

# Visualize the local atomic density

mlab.pipeline.volume(mlab.pipeline.gaussian_splatter(pts))

mlab.view(49, 31.5, 52.8, (4.2, 37.3, 20.6))

mlab.show()