def test_nxvascDijkstra():
"""tests the information gathered using the Dijkstra algorithm"""
import nxvasc
import dicom
import numpy as na
import fomFuncs
import costFuncs
#img=io.readImage("PE00026Filter0_seg.mha", returnITK=False, imgMode='uchar')
#mask = na.where(img>0,1,0)
img=dicom.read_file("data/tree2.dcm")
array=img.pixel_array
mask = na.where(array>0, 1, 0)
vx = nxvasc.nxvasc()
vx.setMask(mask)
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getDijkstraPaths(verbose=True) #uses the dijkstra algorithm to find paths
vx.traceBackPaths()
print("trace back paths")
vx.pruneSplitPaths()
print("prune paths")
vx.showPaths(list(vx.paths.values()))
input('continue')
vx.showPaths(vx.splitPaths,label="Orthogonal MIP Images with Centerlines")
input('continue')
vx.plotPaths(vx.splitPaths,label="firstPlot",show=True)
input('continue')
print("FINISHED")
def test_critEnds():
import nxvasc
import dicom
import fomFuncs
import costFuncs
import imageTools.ITKUtils.io as io
#img=dicom.read_file("data/tree2.dcm")
#array=img.pixel_array
#mask = na.where(array>0, 1, 0)
img = io.readImage("data/p16Mask.mha",returnITK=False,imgMode="uchar")
mask = na.where(img>0,1,0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.endPointsFromCritPoints("data/ends.pckle")
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths5()
print("plot paths")
vx.showPaths(vx.getSGPaths(),label="Orthogonal MIP Images with Centerlines-SG")
input('continue')
def test_mipEndPoints():
import nxvasc
import dicom
import fomFuncs
import costFuncs
img=dicom.read_file("data/tree2.dcm")
array=img.pixel_array
mask = na.where(array>0, 1, 0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.define3x3_2DEndpointKernels()
vx.endPointDetection2D()
vx.viewEndPoints()
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths4()
print("prune paths")
vx.pruneSplitPaths()
print("plot paths")
vx.showPaths(list(vx.paths.values()))
input('continue')
vx.showPaths(vx.splitPaths,label="Orthogonal MIP Images with Centerlines")
input('continue')
vx.plotPaths(vx.splitPaths,label="firstPlot",show=True)
input('continue')
def test_nxvasc1():
"""Uses an exhaustive search for determining the bifurcation points"""
import nxvasc
import fomFuncs
import costFuncs
import dicom
img=dicom.read_file("data/tree2.dcm")
array=img.pixel_array
mask = na.where(array>0, 1, 0)
#mask = createPhantom2()
vx = nxvasc.nxvasc() #calls the class nxvasc and fxn 3x3matrix
vx.setOutputFile("testnx1.out") #Sets the objects mask to image located in the maskfile
vx.setMask(mask)
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getDijkstraPaths(verbose=True)
temp1 = copy.deepcopy(vx.paths)
t4_start = time.time()
vx.traceBackPaths4()
t4_stop = time.time()
vx.paths = copy.deepcopy(temp1)
t2_start = time.time()
vx.traceBackPaths2()
t2_stop = time.time()
vx.paths = copy.deepcopy(temp1)
t1_start = time.time()
vx.traceBackPaths1()
t1_stop = time.time()
print("time for algorithm4:",t4_stop-t4_start)
print("time for algorithm2:",t2_stop-t2_start)
print("time for algorithm1:",t1_stop-t1_start)
def test_critEnds():
import nxvasc
import dicom
import fomFuncs
import costFuncs
import imageTools.ITKUtils.io as io
#img=dicom.read_file("data/tree2.dcm")
#array=img.pixel_array
#mask = na.where(array>0, 1, 0)
img = io.readImage("data/p16Mask.mha", returnITK=False, imgMode="uchar")
mask = na.where(img > 0, 1, 0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.endPointsFromCritPoints("data/ends.pckle")
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths5()
print("plot paths")
vx.showPaths(vx.getSGPaths(),
label="Orthogonal MIP Images with Centerlines-SG")
input('continue')
def test_mipEndPoints():
import nxvasc
import dicom
import fomFuncs
import costFuncs
img = dicom.read_file("data/tree2.dcm")
array = img.pixel_array
mask = na.where(array > 0, 1, 0)
#mask = createPhantom3()
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.getMaskMips()
vx.define3x3_2DEndpointKernels()
vx.endPointDetection2D()
vx.viewEndPoints()
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getEndPointPaths()
print("trace back paths")
vx.traceBackPaths4()
print("prune paths")
vx.pruneSplitPaths()
print("plot paths")
vx.showPaths(list(vx.paths.values()))
input('continue')
vx.showPaths(vx.splitPaths, label="Orthogonal MIP Images with Centerlines")
input('continue')
vx.plotPaths(vx.splitPaths, label="firstPlot", show=True)
input('continue')
def A_Star():
img=io.readImage("PE00025Filter0_seg.mha", returnITK=False, imgMode='uchar')
mask=na.where(img>0,1,0)
instance=nxvasc.nxvasc()
instance.setmask(mask)
indexes=open("Indexes.pckle",'rb')
inds=pickle.load(indexes)
graph=open("PE00025NXgraph.pckle",'rb')
G=pickle.load(graph)
instance.generateRawFigureOfMerit()
instance.setFigureOfMeritF(fomFuncs.fom1)
instance.setCostFunction(costFuncs.cf1)
instance.GenerateAstarPaths(G, inds)
path=open("PE00025A_StarPaths.pckle", 'wb')
pickle.dump(self.paths, path)
def setUp(self):
"""setUp is called before each test is run, tearDown is called after"""
self.img = dicom.read_file("data/tree2.dcm")
self.array = self.img.pixel_array
self.mask = na.where(self.array > 0, 1, 0)
self.vx = nxvasc.nxvasc()
self.vx.setMask(self.mask)
self.vx.getMaskMips()
self.vx.define3x3_2DEndpointKernels()
self.vx.endPointDetection2D()
self.vx.generateRawFigureOfMeritData()
self.vx.setFigureOfMeritF(fomFuncs.fomf1)
self.vx.setCostFunction(costFuncs.cf1)
self.vx.createMaskDictionary()
self.vx.fillFigureOfMerit()
self.vx.createNXGraph(verbose=True)
def A_Star():
img = io.readImage("PE00025Filter0_seg.mha",
returnITK=False,
imgMode='uchar')
mask = na.where(img > 0, 1, 0)
instance = nxvasc.nxvasc()
instance.setmask(mask)
indexes = open("Indexes.pckle", 'rb')
inds = pickle.load(indexes)
graph = open("PE00025NXgraph.pckle", 'rb')
G = pickle.load(graph)
instance.generateRawFigureOfMerit()
instance.setFigureOfMeritF(fomFuncs.fom1)
instance.setCostFunction(costFuncs.cf1)
instance.GenerateAstarPaths(G, inds)
path = open("PE00025A_StarPaths.pckle", 'wb')
pickle.dump(self.paths, path)
def test_nxvasc():
"""Uses sets for determining biforcation points"""
import nxvasc
import fomFuncs
import costFuncs
import dicom
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getDijkstraPaths(verbose=True)
vx.traceBackPaths()
vx.pruneSplitPaths()
vx.plotPaths(label="firstPlot",show=True)
print("FINISHED")
def test_nxvasc():
"""Uses sets for determining biforcation points"""
import nxvasc
import fomFuncs
import costFuncs
import dicom
vx = nxvasc.nxvasc()
vx.setOutputFile("testnx.out")
vx.setMask(mask)
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getDijkstraPaths(verbose=True)
vx.traceBackPaths()
vx.pruneSplitPaths()
vx.plotPaths(label="firstPlot", show=True)
print("FINISHED")
def test_nxvasc1():
"""Uses an exhaustive search for determining the bifurcation points"""
import nxvasc
import fomFuncs
import costFuncs
import dicom
img = dicom.read_file("data/tree2.dcm")
array = img.pixel_array
mask = na.where(array > 0, 1, 0)
#mask = createPhantom2()
vx = nxvasc.nxvasc() #calls the class nxvasc and fxn 3x3matrix
vx.setOutputFile(
"testnx1.out") #Sets the objects mask to image located in the maskfile
vx.setMask(mask)
vx.generateRawFigureOfMeritData()
vx.setFigureOfMeritF(fomFuncs.fomf1)
vx.setCostFunction(costFuncs.cf1)
vx.createMaskDictionary()
vx.fillFigureOfMerit()
vx.createNXGraph(verbose=True)
vx.getDijkstraPaths(verbose=True)
temp1 = copy.deepcopy(vx.paths)
t4_start = time.time()
vx.traceBackPaths4()
t4_stop = time.time()
vx.paths = copy.deepcopy(temp1)
t2_start = time.time()
vx.traceBackPaths2()
t2_stop = time.time()
vx.paths = copy.deepcopy(temp1)
t1_start = time.time()
vx.traceBackPaths1()
t1_stop = time.time()
print("time for algorithm4:", t4_stop - t4_start)
print("time for algorithm2:", t2_stop - t2_start)
print("time for algorithm1:", t1_stop - t1_start)
import os
import sys
sys.path.append('../')
import nxvasc
import pickle
import numpy as na
import scipy
import math
import imageTools.ITKUtils.io as io
#Read in modified .pckle file from 5x5endpointMatch.py
endpoints=open('Modifiedf0MipEndpoints5x5.pckle','rb')
crds=pickle.load(endpoints)
img= io.readImage('PE00026Filter0_seg.mha', returnITK=False, imgMode='uchar')
mask = na.where(img>0, 1, 0) #create the mask
instance=nxvasc.nxvasc()#create an instance of the class
output=open("Configuration5x5.pckle", 'wb') #Need to store the output for the 5x5
instance.setMask(mask) #Set the mask using the numpy image mask. In addition to
#reading the mask, the 1D indicies into the 3D image are generate and stored
instance.createMaskDictionary() #Create dictionary that maps the index into the 3D
#volume to an index into the mask. The 1D array into the 3D volume is the key &
#the ordinal position of the index is the value
instance.get5x5matrix() #define relationship between neighbors
#get the crds of all pts within the 5x5 neighborhood of index (i)
Neighbors=[] #to store arrays of neighbors for each point
for crd in crds: \
#grab the 5x5x5 neighborhood
neighbors = mask[crd[2]-2:crd[2]+3, crd[1]-2:crd[1]+3, crd[0]-2:crd[0]+3]
Neighbors.append(neighbors)
pickle.dump([Neighbors, crds], output)
the actual endpoint. The output is saved as Configuration7X7.pckle and contains the neighbors of the identified true endpoints,
whether it is the original point or replaced by a neighbor in endpointMatch.py. Gets the 7X7X7 neighborhood of the neighbors"""
import os
import sys
sys.path.append('../../vasctrees')
import nxvasc
import pickle
import numpy as na
import scipy
import math
import imageTools.ITKUtils.io as io
#Read in modified .pckle file from 7x7endpointMatch.py
endpoints = open('Modifiedf0MipEndpoints.pckle', 'rb')
crds = pickle.load(endpoints)
img = io.readImage('PE00014Filter0_seg.mha', returnITK=False, imgMode='uchar')
mask = na.where(img > 0, 1, 0) #create the mask
instance = nxvasc.nxvasc() #create an instance of the class
output = open("Configuration7x7.pckle", 'wb')
instance.setMask(mask)
instance.createMaskDictionary()
instance.get7x7matrix()
#get the crds of all pts within the 7x7 neighborhood of index (i)
Neighbors = [] #to store arrays of neighbors for each point
for crd in crds:
neighbors = mask[crd[2] - 3:crd[2] + 4, crd[1] - 3:crd[1] + 4,
crd[0] - 3:crd[0] + 4]
Neighbors.append(neighbors)
pickle.dump([Neighbors, crds], output)
import numpy as na
import random
import pickle
import imageTools.ITKUtils.io as io
import nxvasc
from sliceOrientation import OrientMask
img = io.readImage("PE00001Filter0_seg.mha", returnITK=False, imgMode="uchar")
nonPoints = open("PE00001NonEndpoints.pckle", 'rb')
nonendpoints = pickle.load(
nonPoints) #points from ModifiedGenerateNonEndpoints.py
output2 = open("PE00001NeighborsNonPoints5x5.pckle",
'wb') #store 5x5 neighborhood
#Must create the mask so we can identify the points that are not endpoints
mask = na.where(img > 0, 1, 0)
point = nxvasc.nxvasc()
point.setMask(mask)
point.createMaskDictionary()
point.get5x5matrix()
crds = point.get_crds(nonendpoints)
ModifiedSampledPoints = []
ModifiedPoints = []
for crd in crds:
print(crd)
#get the neighbors to add up the neighborhood
neighbors = mask[crd[2] - 2:crd[2] + 3, crd[1] - 2:crd[1] + 3,
crd[0] - 2:crd[0] + 3]
print(neighbors)
ModifiedSampledPoints.append(neighbors)
for M in ModifiedSampledPoints:
#print M
import os
import sys
sys.path.append("../vasctrees")
import nxvasc
from sliceOrientation import OrientMask
import imageTools.ITKUtils.io as io
import scipy.ndimage as ndi
import numpy as na
import pickle
fle=open("PE00001NonEndpoints.pckle",'wb')
output2=open("PE00001NeighborsNonPoints7x7.pckle", 'wb')
img=io.readImage("PE00001Filter0_seg.mha", returnITK=False, imgMode="uchar")
#Must create the mask so we can identify the points that are not endpoints
mask=na.where(img>0, 1, 0)
point=nxvasc.nxvasc()
point.setMask(mask)
point.createMaskDictionary()
point.get7x7matrix()
f1=ndi.distance_transform_cdt(img)
surface=na.where(f1==1,1,0)
indsSurface =na.nonzero(surface.flat==1)[0]
length=len(indsSurface)
print("length",length)
ModifiedSampledPoints=[]
ModifiedPoints=[]
points=[]
for i in indsSurface:
if len(ModifiedPoints)<112:
crds=point.get_crds(i)
print(crds)
