import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
_check_topology_lut_dir, _check_available_memory
[docs]def stack_intensity_mapping(image, references, mapped, weights = None,
patch=2, search=3,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" Stack intensity mapping
Uses a simple non-local means approach adapted from [1]_
Parameters
----------
image: niimg
Input 2D image
references: [niimg]
Reference 2D images to use for intensity mapping
mapped: [niimg]
Corresponding mapped 2D images to use for intensity mapping
weights: [float], optional
Weight factors for the 2D images (default is 1 for all)
patch: int, optional
Maximum distance to define patch size (default is 2)
search: int, optional
Maximum distance to define search window size (default is 3)
save_data: bool
Save output data to file (default is False)
overwrite: bool
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* result (niimg): The intensity mapped input
Notes
----------
Original Java module by Pierre-Louis Bazin.
References
----------
.. [1] P. Coupé, J.V. Manjón, V. Fonov, J. Pruessner, M. Robles, D.L. Collins,
Patch-based segmentation using expert priors: Application to hippocampus
and ventricle msegmentation, NeuroImage, vol. 54, pp. 940--954, 2011.
"""
print('\nStack Intensity Mapping')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, image)
result_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=image,
suffix='sim-img'))
if overwrite is False \
and os.path.isfile(result_file) :
print("skip computation (use existing results)")
output = {'result': result_file}
return output
# start virtual machine, if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# create instance
sim = nighresjava.NonlocalIntensityMapping()
# set parameters
# load image and use it to set dimensions and resolution
img = load_volume(image)
data = img.get_fdata()
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = data.shape
sim.setDimensions(dimensions[0], dimensions[1], 1)
sim.setInputImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
sim.setReferenceNumber(len(references))
for idx,ref in enumerate(references):
data = load_volume(ref).get_fdata()
sim.setReferenceImageAt(idx,nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
data = load_volume(mapped[idx]).get_fdata()
sim.setMappedImageAt(idx,nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
if weights is not None:
sim.setWeightAt(idx, weights[idx])
else:
sim.setWeightAt(idx, 1.0)
# set algorithm parameters
sim.setPatchDistance(patch)
sim.setSearchDistance(search)
# execute the algorithm
try:
sim.execute2D()
except:
# if the Java module fails, reraise the error it throws
print("\n The underlying Java code did not execute cleanly: ")
print(sys.exc_info()[0])
raise
return
# reshape output to what nibabel likes
data = np.reshape(np.array(sim.getMappedImage(),
dtype=np.float32), dimensions, 'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_min'] = np.nanmin(data)
header['cal_max'] = np.nanmax(data)
result = nb.Nifti1Image(data, affine, header)
if save_data:
save_volume(result_file, result)
return {'result': result_file}
else:
return {'result': result}