Source code for nighres.filtering.recursive_ridge_diffusion

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_atlas_file, \

[docs]def recursive_ridge_diffusion(input_image, ridge_intensities, ridge_filter, surface_levelset=None, orientation='undefined', loc_prior=None, min_scale=0, max_scale=3, diffusion_factor=1.0, similarity_scale=0.1, max_iter=100, max_diff=1e-3, threshold=0.5, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Recursive Ridge Diffusion Extracts planar of tubular structures across multiple scales, with an optional directional bias. Parameters ---------- input_image: niimg Input image ridge_intensities: {'bright','dark','both'} Which intensities to consider for the filtering ridge_filter: {'2D','1D','0D'} Whether to filter for 2D ridges, 1D vessels, or 0D holes surface_levelset: niimg, optional Level set surface to restrict the orientation of the detected features orientation: {'undefined','parallel','orthogonal'} The orientation of features to keep with regard to the surface or its normal loc_prior: niimg, optional Location prior image to restrict the search for features min_scale: int Minimum scale (in voxels) to look for features (default is 0) max_scale: int Maximum scale (in voxels) to look for features (default is 3) diffusion_factor: float Scaling factor for the diffusion weighting in [0,1] (default is 1.0) similarity_scale: float Scaling of the similarity function as a factor of intensity range max_iter: int Maximum number of diffusion iterations max_diff: int Maximum difference to stop the diffusion threshold: float Detection threshold for the structures to keep (default is 0.5) 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) * filter (niimg): raw filter response (_rrd-filter) * propagation (niimg): propagated probabilistic response after diffusion (_rrd-propag) * scale (niimg): scale of the detection filter (_rrd-scale) * ridge_dir (niimg): estimated local ridge direction (_rrd-dir) * ridge_pv (niimg): ridge partial volume map, taking size into account (_rrd-pv) * ridge_size (niimg): estimated size of each detected component (rrd-size) Notes ---------- Original Java module by Pierre-Louis Bazin. Extension of the recursive ridge filter in [1]_. References ---------- .. [1] Bazin et al (2016), Vessel segmentation from quantitative susceptibility maps for local oxygenation venography, Proc ISBI. """ print('\n Recursive Ridge Diffusion') # check atlas_file and set default if not given #atlas_file = _check_atlas_file(atlas_file) # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, input_image) filter_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-filter')) propagation_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-propag')) scale_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-scale')) ridge_direction_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-dir')) ridge_pv_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-pv')) ridge_size_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='rrd-size')) if overwrite is False \ and os.path.isfile(filter_file) \ and os.path.isfile(propagation_file) \ and os.path.isfile(scale_file) \ and os.path.isfile(ridge_direction_file) \ and os.path.isfile(ridge_pv_file) \ and os.path.isfile(ridge_size_file) : print("skip computation (use existing results)") output = {'filter': filter_file, 'propagation': propagation_file, 'scale': scale_file, 'ridge_dir': ridge_direction_file, 'ridge_pv': ridge_pv_file, 'ridge_size': ridge_size_file} return output # load input image and use it to set dimensions and resolution img = load_volume(input_image) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape if (len(dimensions)<3): dimensions = (dimensions[0], dimensions[1], 1) if (len(resolution)<3): resolution = [resolution[0], resolution[1], 1.0] # start virtual machine, if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # create extraction instance if dimensions[2] is 1: rrd = nighresjava.FilterRecursiveRidgeDiffusion2D() else: rrd = nighresjava.FilterRecursiveRidgeDiffusion() # set parameters rrd.setRidgeIntensities(ridge_intensities) rrd.setRidgeFilter(ridge_filter) rrd.setOrientationToSurface(orientation) rrd.setMinimumScale(min_scale) rrd.setMaximumScale(max_scale) rrd.setDiffusionFactor(diffusion_factor) rrd.setSimilarityScale(similarity_scale) rrd.setPropagationModel("none") if max_iter>0: rrd.setPropagationModel("diffusion") rrd.setMaxIterations(max_iter) rrd.setMaxDifference(max_diff) rrd.setDetectionThreshold(threshold) rrd.setDimensions(dimensions[0], dimensions[1], dimensions[2]) rrd.setResolutions(resolution[0], resolution[1], resolution[2]) # input input_image rrd.setInputImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # input surface_levelset : dirty fix for the case where surface image not input try: data = load_volume(surface_levelset).get_data() rrd.setSurfaceLevelSet(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) except: print("no surface image") # input location prior image : loc_prior is optional try: data = load_volume(loc_prior).get_data() rrd.setLocationPrior(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) except: print("no location prior image") # execute Extraction try: rrd.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 filter_data = np.reshape(np.array(rrd.getFilterResponseImage(), dtype=np.float32), dimensions, 'F') propagation_data = np.reshape(np.array(rrd.getPropagatedResponseImage(), dtype=np.float32), dimensions, 'F') scale_data = np.reshape(np.array(rrd.getDetectionScaleImage(), dtype=np.int32), dimensions, 'F') if dimensions[2] is 1: ridge_direction_data = np.reshape(np.array(rrd.getRidgeDirectionImage(), dtype=np.float32), (dimensions[0],dimensions[1],2), 'F') else: ridge_direction_data = np.reshape(np.array(rrd.getRidgeDirectionImage(), dtype=np.float32), (dimensions[0],dimensions[1],dimensions[2],3), 'F') ridge_pv_data = np.reshape(np.array(rrd.getRidgePartialVolumeImage(), dtype=np.float32), dimensions, 'F') ridge_size_data = np.reshape(np.array(rrd.getRidgeSizeImage(), dtype=np.float32), dimensions, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_max'] = np.nanmax(filter_data) filter_img = nb.Nifti1Image(filter_data, affine, header) header['cal_max'] = np.nanmax(propagation_data) propag_img = nb.Nifti1Image(propagation_data, affine, header) header['cal_max'] = np.nanmax(scale_data) scale_img = nb.Nifti1Image(scale_data, affine, header) header['cal_max'] = np.nanmax(ridge_direction_data) ridge_dir_img = nb.Nifti1Image(ridge_direction_data, affine, header) header['cal_max'] = np.nanmax(ridge_pv_data) ridge_pv_img = nb.Nifti1Image(ridge_pv_data, affine, header) header['cal_max'] = np.nanmax(ridge_size_data) ridge_size_img = nb.Nifti1Image(ridge_size_data, affine, header) if save_data: save_volume(filter_file, filter_img) save_volume(propagation_file, propag_img) save_volume(scale_file, scale_img) save_volume(ridge_direction_file, ridge_dir_img) save_volume(ridge_pv_file, ridge_pv_img) save_volume(ridge_size_file, ridge_size_img) return {'filter': filter_file, 'propagation': propagation_file, 'scale': scale_file, 'ridge_dir': ridge_direction_file, 'ridge_pv': ridge_pv_file, 'ridge_size': ridge_size_file} else: return {'filter': filter_img, 'propagation': propag_img, 'scale': scale_img, 'ridge_dir': ridge_dir_img, 'ridge_pv': ridge_pv_img, 'ridge_size': ridge_size_img}