Source code for nighres.segmentation.fuzzy_cmeans

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_available_memory

[docs]def fuzzy_cmeans(image, clusters=3, max_iterations=50, max_difference=0.01, smoothing=0.1, fuzziness=2.0, mask_zero=True, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Fuzzy C-means image segmentation Estimates intensity clusters with spatial smoothness and partial voluming. Based on the RFCM algorithm of (Pham, 2001). Parameters ---------- image: niimg Input image to segment clusters: int Number of clusters to estimate (default is 3) max_iterations: int Maximum number of iterations to perform (default is 50) max_difference: float Maximum difference between steps for stopping (default is 0.01) smoothing: float Ratio of spatial smoothness to impose on the clusters (default is 0.1) fuzziness: float Scaling of the C-means measure, in [1.0 - 3.0] (default is 2.0) mask_zero: bool Whether to ignore zero values (default is true) 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) * memberships [niimg]: List of membership functions for each cluster in [0,1] (_rfcm-mem#cluster) * classification (niimg): Hard classification of most likely cluster per voxel (_rfcm-class) Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nFuzzy C-means') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) mem_files = [] for c in range(clusters): mem_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='rfcm-mem'+str(c+1), )) mem_files.append(mem_file) classification_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='rfcm-class')) if overwrite is False \ and os.path.isfile(classification_file): missing = False for mem_file in mem_files: if not os.path.isfile(mem_file): missing = True if not missing: print("skip computation (use existing results)") output = {'classification': classification_file, 'memberships': mem_files} 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 rfcm = nighresjava.FuzzyCmeans() # set parameters rfcm.setClusterNumber(clusters) rfcm.setSmoothing(smoothing) rfcm.setFuzziness(fuzziness) rfcm.setMaxDist(max_difference) rfcm.setMaxIter(max_iterations) # load target image for parameters print("load: "+str(image)) img = load_volume(image) data = img.get_data() affine = img.get_affine() header = img.get_header() resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape if len(dimensions)>2: rfcm.setDimensions(dimensions[0], dimensions[1], dimensions[2]) else: rfcm.setDimensions(dimensions[0], dimensions[1], 1) if len(resolution)>2: rfcm.setResolutions(resolution[0], resolution[1], resolution[2]) else: rfcm.setResolutions(resolution[0], resolution[1], resolution[1]) # image rfcm.setImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # execute try: if mask_zero: rfcm.initZeroMaskImage() else: rfcm.initBasicMaskImage() rfcm.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 classification_data = np.reshape(np.array(rfcm.getClassification(), dtype=np.int32), dimensions, 'F') header['cal_max'] = np.nanmax(classification_data) classification = nb.Nifti1Image(classification_data, affine, header) memberships = [] for c in range(clusters): mem_data = np.reshape(np.array(rfcm.getMembership(c), dtype=np.float32), dimensions, 'F') header['cal_max'] = np.nanmax(mem_data) membership = nb.Nifti1Image(mem_data, affine, header) memberships.append(membership) if save_data: save_volume(classification_file, classification) for c in range(clusters): save_volume(mem_files[c], memberships[c]) output= {'classification': classification_file, 'memberships': mem_files} else: output= {'classification': classification, 'memberships': memberships} return output