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_regularisation(image, cutoff=50, mask=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" Stack intensity regularisation
Estimates an image-to-image linear intensity scaling for a stack of 2D images
Parameters
----------
image: niimg
Input 2D images, stacked in the Z dimension
cutoff: float, optional
Range of image differences to keep (default is middle 50%)
mask: niimg
Input mask or probability image of the data to use (optional)
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 regularised input
Notes
----------
Original Java module by Pierre-Louis Bazin.
"""
print('\nStack Intensity Regularisation')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, image)
regularised_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=image,
suffix='sir-img'))
if overwrite is False \
and os.path.isfile(regularised_file) :
print("skip computation (use existing results)")
output = {'result': regularised_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
sir = nighresjava.StackIntensityRegularisation()
# set parameters
# load image and use it to set dimensions and resolution
img = load_volume(image)
data = img.get_data()
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = data.shape
sir.setDimensions(dimensions[0], dimensions[1], dimensions[2])
sir.setInputImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
if mask is not None:
sir.setForegroundImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
# set algorithm parameters
sir.setVariationRatio(float(cutoff))
# execute the algorithm
try:
sir.execute()
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
regularised_data = np.reshape(np.array(sir.getRegularisedImage(),
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(regularised_data)
header['cal_max'] = np.nanmax(regularised_data)
regularised = nb.Nifti1Image(regularised_data, affine, header)
if save_data:
save_volume(regularised_file, regularised)
return {'result': regularised_file}
else:
return {'result': regularised}