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updates to run spectral clustering in one shot
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@XeniosP
XeniosP committed Mar 20, 2024
1 parent d3f1c18 commit bdf67a6
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Showing 4 changed files with 295 additions and 3 deletions.
6 changes: 3 additions & 3 deletions biswebpython/config/requirements.txt
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@@ -1,6 +1,6 @@
nibabel==3.2.2
numpy>=1.22.0
pillow>=4.3.0
nibabel
numpy
pillow
scipy
setuptools
wheel>=0.31
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1 change: 1 addition & 0 deletions biswebpython/core/bis_objects.py
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Expand Up @@ -263,6 +263,7 @@ def saveBinary(self,fname):
with open(fname, 'wb') as fp:
fp.write(total);
print('++++ binary matrix saved in',fname,len(total));
self.filename=fname;


# --------------------------------------
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1 change: 1 addition & 0 deletions biswebpython/modules/PythonModuleList.txt
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Expand Up @@ -16,6 +16,7 @@ extractSlice
flipImage
gradientImage
individualizedParcellation
imageSpectralClustering
linearRegistration
morphologyFilter
nonlinearRegistration
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290 changes: 290 additions & 0 deletions biswebpython/modules/imageSpectralClustering.py
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@@ -0,0 +1,290 @@
# LICENSE
#
# _This file is Copyright 2018 by the Image Processing and Analysis Group (BioImage Suite Team). Dept. of Radiology & Biomedical Imaging, Yale School of Medicine._
#
# BioImage Suite Web is licensed under the Apache License, Version 2.0 (the "License");
#
# - you may not use this software except in compliance with the License.
# - You may obtain a copy of the License at [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)
#
# __Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.__
#
# ENDLICENSE

import biswebpython.core.bis_basemodule as bis_basemodule
import biswebpython.core.bis_baseutils as bis_baseutils
import biswebpython.core.bis_objects as bis_objects
from biswebpython.modules.extractImagePatches import *
import os

class imageSpectralClustering(bis_basemodule.baseModule):

def __init__(self):
super().__init__();
self.name='imageDistanceMatrix';

def createDescription(self):

return {
"name": "compute spectral Imaging CLustering (Calls matlab code)",
"description": "Given an image and a mask compute the image distance matrix, the index map and a matlab script to run the clustering code",
"author": "Xenios Papademetris and Xilin Shen",
"version": "1.0",
"inputs": [
{
"type": "image",
"name": "Input Image",
"description": "The input (timeseries) image",
"varname": "input",
"shortname" : "i",
"required": True
},
{
"type": "image",
"name": "Objectmap Image",
"description": "The objectmap/mask image",
"varname": "mask",
"shortname" : "m",
"required": False
},
],
"outputs": [
{
'type': 'matrix',
'name': 'Output Matrix',
'description': 'the output distance matrix',
'varname': 'output',
'shortname': 'o',
'required': True,
'extension' : ".binmatr"
},
{
'type': 'image',
'name': 'IndexMap Image',
'description': 'the output indexmap image',
'varname': 'indexmap',
'shortname': 'x',
'required': False,
'extension' : ".nii.gz"
}
],
"params": [
{
"name": "useradius",
"description": "If true use radius else sparsity",
"varname": "useradius",
"type": "boolean",
"default": True
},
{
"name": "NumThreads",
"description": "The number of threads to use",
"type": "int",
"default": 1,
"lowbound": 1,
"highbound": 10,
"varname": "numthreads"
},
{
"name": "Radius",
"description": "The radius constraint (if useradius=true)",
"type": "float",
"default": 4.0,
"lowbound": 0.1,
"highbound": 10.0,
"varname": "radius"
},
{
"name": "Sparsity",
"description": "The sparsity constraint (if useradius=false)",
"type": "float",
"default": 0.01,
"lowbound": 0.01,
"highbound": 0.2,
"varname": "sparsity"
},
{
"name": "Numpatches",
"description": "Number of patches to extract (default=0 i.e. use whole image as opposed to patches)",
"type": "int",
"default": 0,
"lowbound": 0,
"highbound": 65536,
"varname": "numpatches"
},
{
"name": "Smoothness",
"description": "The weight of the euclidean smoothness constraint for clustering",
"type": "float",
"default": 0.01,
"lowbound": 0.0,
"highbound": 10.0,
"varname": "smoothness"
},
{
"name": "Numclusters",
"description": "Number of clusters",
"type": "int",
"default": 3,
"lowbound": 2,
"highbound": 500,
"varname": "numclusters"
},
{
"name": "Patchsize",
"description": "Patch size (in voxels) (default=32) if using patches",
"type": "int",
"default": 32,
"lowbound": 2,
"highbound": 256,
"varname": "patchsize"
},
{
"name": "3d",
"description": "if true 3d patches (default=false) if using patches",
"priority": 1000,
"advanced": False,
"gui": "check",
"varname": "threed",
"type": 'boolean',
"default": False,
},
{
"name": "Matlab script",
"description": "name of output matlab script",
"type": "string",
"default": None,
"varname": "script"
},
{
"name": "Matlab cluster output image",
"description": "name of matlab output cluster image. If None then script+.nii.gz",
"type": "string",
"default": None,
"varname": "clusteroutput"
},
{
"name": "Matlab path",
"description": "path to bisweb matlab library",
"type": "string",
"default": None,
"varname": "matlabpath"
},

{
"name": "runmatlab",
"description": "If true try to execute matlab",
"varname": "runmatlab",
"type": "boolean",
"default": True
},
bis_baseutils.getDebugParam()
],
}


def saveOutputs(self,inputparameters={}):
f=super().saveOutputs(inputparameters);
if f==False:
return False;


vals=self.innervalues;
if vals['script'] is None:
return true;


matlabpath=vals['matlabpath'];
clusteroutput=vals['clusteroutput'];

if matlabpath is None:
matlabpath=os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))+'/matlab';
print('++++\t auto setting matlabpath to',matlabpath)

if clusteroutput is None:
clusteroutput=vals['script']+".nii.gz";
print('++++\t auto setting clusteroutput to',clusteroutput)
else:
print('++++\t using clusteroutput as',clusteroutput);

out="addpath('"+matlabpath+"');\n";
out=out+"bispath();\n";
out=out+"dist=bis_matrix();\n"
out=out+"w=dist.loadbinary('"+self.outputs['output'].filename+"');\n";
out=out+"indexmap=bis_image('"+self.outputs['indexmap'].filename+"');\n";
out=out+"indexmap.print();";
out=out+"output=bis_distmatrixparcellation(w,indexmap,"+str(vals['numclusters'])+","+str(vals['smoothness'])+");\n"
out=out+"output.save('"+clusteroutput+"');\nexit\n";
try:

with open(vals['script'], 'w') as fp:
fp.write(out);
print('++++\t Saved matlab script in',vals['script']);
except:
print("Failed to open",vals['script']);
return False;


cmd="matlab -nodisplay -nosplash -nodesktop -r \"run('"+vals['script']+"');exit;\"";
print('++++ to run matlab type:',cmd)
if vals['runmatlab']:
print(out);
os.system(cmd);

return True;



def directInvokeAlgorithm(self,vals):
print('oooo invoking: imageDistanceMatrix with vals', vals);


if (vals['numpatches']>0):
print('_____________________________________________');
print('____ First extracting patches')

patchExtractor=extractImagePatches();
patchExtractor.execute({ 'input' : self.inputs['input'] },
{ 'numpatches' : vals['numpatches'],
'patchsize' : vals['patchsize'],
'threed' : vals['threed'],
'ordered' : False
});
self.inputs['input']=patchExtractor.getOutputObject('output');
self.inputs['mask']=0;
print('_____________________________________________');


paramobj= {
'numthreads' : vals['numthreads'],
'sparsity' : vals['sparsity'],
'radius' : vals['radius'],
'useradius' : self.parseBoolean(vals['useradius'])

};

out=bis_baseutils.getDynamicLibraryWrapper().computeImageDistanceMatrixWASM(self.inputs['input'],
self.inputs['mask'],
paramobj,
self.parseBoolean(vals['debug']));
self.outputs['output']=bis_objects.bisMatrix();
self.outputs['output'].create(out);

self.outputs['indexmap']=bis_baseutils.getDynamicLibraryWrapper().computeImageIndexMapWASM(self.inputs['mask'],
self.parseBoolean(vals['debug']));

# Propagate Orientation in this weird matrix to image thing
self.outputs['indexmap'].affine=self.inputs['mask'].affine;

self.innervalues=vals;


return True




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