def ac(
NAME,
priorSeg,
followUpScan,
followUpSegName,
alpha=1e5,
sigma=3,
smoothing=3,
threshold=0.01,
balloon=1.0,
iters=45,
):
"Perform active contour on initial warped follow-up segmentation (to finalize segmentation)."
print "Performing active contour finalization of follow-up segmentation..."
input_fixed = followUpScan
gray = nib.load(input_fixed)
graydata = gray.get_data()
# do 99% norm to 1000
origDATA = np.array(graydata, dtype=float)
origDATA = origDATA * 1000 / np.percentile(origDATA, 99)
input_seg = NAME + "/" + os.path.basename(priorSeg[:-7]) + "0to1.nii.gz"
seg = nib.load(input_seg)
segdata = seg.get_data()
affine = seg.get_affine()
new_image_preAC = nib.Nifti1Image(segdata, affine)
nib.save(new_image_preAC, followUpSegName[:-7] + "_preAC.nii.gz")
img = segdata > 0.2
[L, M, N] = np.shape(img)
# Active Contour 2D
finalseg2 = img.copy() * 0
for n in range(0, N):
mgac = []
img2 = origDATA[:, :, n]
gI = msnake.gborders(img2, alpha, sigma)
# Morphological GAC. Initialization of the level-set.
mgac = msnake.MorphGAC(gI, smoothing, threshold, balloon)
mgac.levelset = img[:, :, n] > 0.2
for ijk123 in xrange(iters): # num iterations
mgac.step()
finalseg2[:, :, n] = mgac.levelset
# Light filtering
finalSeg = (gaussian_filter(finalseg2.copy() * 255, sigma=[3, 3, 1])) > 100
im_data = origDATA.copy()
# Add now narrow band sobel/watershed technique.
for i in xrange(0, N):
img = im_data[:, :, i]
segslice = finalSeg[:, :, i]
if np.max(finalSeg[:, :, i] > 0):
erodeimg = erosion(segslice.copy(), iterations=1)
dilateimg = dilation(segslice.copy(), iterations=1)
seeds = img * 0
seeds[:] = 1
seeds[dilateimg > 0] = 0
seeds[erodeimg > 0] = 2
sobelFilt = sobel(np.array(img.copy(), dtype="int16"))
mgac = watershed(sobelFilt, seeds) > 1
finalSeg[:, :, i] = mgac > 0
# Write the final output
affine = seg.get_affine()
image1 = np.uint8(finalSeg)
new_image = nib.Nifti1Image(image1, affine)
nib.save(new_image, followUpSegName)
def MICOS(fileNAME):
tic = time.clock()
selfz =0
selfz1 = 0
selfz2 = 0
selfrotD = -90
imgObj2= nib.load(str(fileNAME))
imgObj1 = imgObj2
im = imgObj2
selfaffine2 = imgObj2.get_affine()
selfheaderdtype = imgObj2.get_data_dtype()
selfPSx = imgObj1.get_header()['pixdim'][1]
selfPSy = imgObj1.get_header()['pixdim'][2]
selfPSz = imgObj1.get_header()['pixdim'][3]
(x,y,z) = orx.aff2axcodes(selfaffine2)
selfOrx = x
selfOry = y
selfOrz = z
ornt = orx.axcodes2ornt((x,y,z))
refOrnt = orx.axcodes2ornt(('R','S','A')) #was 'R', 'A', 'S'
newOrnt = orx.ornt_transform(ornt,refOrnt)
selfornt = ornt
selfrefOrnt = refOrnt
selfimg_data2 = imgObj2.get_data()
selfimg_data2 = orx.apply_orientation(selfimg_data2,newOrnt)
selfimg_data2 = np.fliplr(np.rot90(selfimg_data2,1))
im_data = selfimg_data2
[x_si,y_si,z_si] = np.shape(im_data)
#do 99% norm to 1000
im_data = np.array(im_data,dtype='float')
im_data = im_data * 1000/np.percentile(im_data,99)
#print np.shape(im_data)
initialSeg = im_data.copy() * 0
#begin user roi drawing...
#go from middle up...
for i in xrange(np.round(z_si/2),z_si,3):
img = (im_data[:,:,i])
# show the image
if i > np.round(z_si/2):
plt.figure(figsize=(ROI1.figwidth,ROI1.figheight))
plt.imshow(img,cmap='gray')
plt.colorbar()
plt.title("outline one kidney, slice = " + str(i))
# let user draw first ROI
ROI1 = polydraw(roicolor='r') #let user draw first ROI
# show the image with the first ROI
plt.figure(figsize=(ROI1.figwidth,ROI1.figheight))
plt.imshow(img,cmap='gray')
plt.colorbar()
ROI1.displayROI()
plt.title("outline other kidney, slice = " + str(i))
# let user draw second ROI
ROI2 = polydraw(roicolor='b') #let user draw ROI
initialSeg[:,:,i] = ROI1.getMask(img) + ROI2.getMask(img)
#go from middle up...
for i in xrange(np.round(z_si/2)-1,0,-3):
img = (im_data[:,:,i])
# show the image
plt.figure(figsize=(ROI1.figwidth,ROI1.figheight))
plt.imshow(img,cmap='gray')
plt.colorbar()
plt.title("outline one kidney, slice = " + str(i))
# let user draw first ROI
ROI1 = polydraw(roicolor='r') #let user draw first ROI
# show the image with the first ROI
plt.figure(figsize=(ROI1.figwidth,ROI1.figheight))
plt.imshow(img,cmap='gray')
plt.colorbar()
ROI1.displayROI()
plt.title("outline other kidney, slice = " + str(i))
# let user draw second ROI
ROI2 = polydraw(roicolor='b') #let user draw ROI
initialSeg[:,:,i] = ROI1.getMask(img) + ROI2.getMask(img)
toc = time.clock()
#save out drawn polygon
aff = selfaffine2
outImage = deepcopy(initialSeg)#np.rot90(np.fliplr(self.segImg),-1)
[x_si,y_si,z_si] = np.shape(outImage)
#print np.shape(outImage)
#This method works (for fastsegs)... but need more robust
#for i in range(0,z_si):
# outImage[:,:,i] = np.rot90(self.segImg[:,:,z_si-1-i],-1)
#try new method (more robust to header and affine mix-ups)
ornt = orx.axcodes2ornt((selfOrx,selfOry,selfOrz))
refOrnt = orx.axcodes2ornt(('R','S','A'))
newOrnt = orx.ornt_transform(refOrnt,ornt) #reversed these
outImage= orx.apply_orientation(np.rot90(np.fliplr(outImage),-1),newOrnt)
#outImage = orx.apply_orientation(outImage,newOrnt)
#outImage = np.rot90(np.fliplr(outImage),-1)
#print np.shape(outImage)
#outImage = np.array(outImage,dtype=selfheaderdtype)
new_image = nib.Nifti1Image(outImage,aff)
nib.save(new_image,fileNAME[:-7]+'_polygon_MICOS.nii.gz')
# Dilate and fill in missing slices
initialSeg = dilation(initialSeg,iterations = 1)
finalSeg = initialSeg.copy() * 0
# now try convex hull method instead to better approximate missing slices (previous method is above)
# This works but way too long. Also, would likely need to do object finding first, so compute
# Convex hull for each kidney separately.
while 0:
xlist,ylist,zlist = find_3D_object_voxel_list(initialSeg)
voxlist = np.zeros(shape=(np.shape(xlist)[0],3),dtype='int16')
voxlist[:,0] = xlist
voxlist[:,1] = ylist
voxlist[:,2] = zlist
tri = dtri(voxlist)
# construct full voxel list
xxlist,yylist,zzlist = find_3D_object_voxel_list((initialSeg+1)>0)
fullvoxlist = np.zeros(shape=(np.shape(xxlist)[0],3),dtype='int16')
fullvoxlist[:,0] = xxlist
fullvoxlist[:,1] = yylist
fullvoxlist[:,2] = zzlist
finalSeg = np.array(in_hull(fullvoxlist,tri),dtype=float)
finalSeg = np.reshape(finalSeg,(x_si,y_si,z_si))
# Now do gaussian blur of polygon to smooth
initialSeg = (filt.gaussian_filter(initialSeg.copy()*255,sigma=[3,3,1])) > 100
#Begin optimized method...
for i in xrange(0,z_si):
img = (im_data[:,:,i])
if np.max(initialSeg[:,:,i]>0):
mgac = []
gI = msnake.gborders(img,alpha=1E5,sigma=3.0) # increasing sigma allows more changes in contour
mgac = msnake.MorphGAC(gI,smoothing=3,threshold=0.01,balloon=0.0) #was 2.5
mgac.levelset = initialSeg[:,:,i]>0.5
for ijk123 in xrange(100):
mgac.step()
finalSeg[:,:,i] = mgac.levelset
#print i
# Now do gaussian blur and threshold to finalize segmentation...
finalSeg = (filt.gaussian_filter(finalSeg.copy()*255,sigma=[3,3,1])) > 100
#using this helps with single slice errors of the active contour
# Try adding now narrow band sobel/watershed technique.
for i in xrange(0,z_si):
img = (im_data[:,:,i])
segslice = finalSeg[:,:,i]
if np.max(finalSeg[:,:,i]>0):
erodeimg = erosion(segslice.copy(),iterations=1)
dilateimg = dilation(segslice.copy(),iterations=1)
seeds = img * 0
seeds[:] = 1
seeds[dilateimg>0] = 0
seeds[erodeimg>0] = 2
sobelFilt = sobel(np.array(img.copy(),dtype='int16'))
mgac = watershed(sobelFilt,seeds)>1
finalSeg[:,:,i] = mgac>0
#save out segmentation
aff = selfaffine2
outImage = deepcopy(finalSeg)#np.rot90(np.fliplr(self.segImg),-1)
outImage = np.array(outImage,dtype='float')
[x_si,y_si,z_si] = np.shape(outImage)
#This method works (for fastsegs)... but need more robust
#for i in range(0,z_si):
# outImage[:,:,i] = np.rot90(self.segImg[:,:,z_si-1-i],-1)
#try new method (more robust to header and affine mix-ups)
ornt = orx.axcodes2ornt((selfOrx,selfOry,selfOrz))
refOrnt = orx.axcodes2ornt(('R','S','A'))
newOrnt = orx.ornt_transform(refOrnt,ornt) #reversed these
outImage= orx.apply_orientation(np.rot90(np.fliplr(outImage),-1),newOrnt)
#outImage = orx.apply_orientation(outImage,newOrnt)
#outImage = np.rot90(np.fliplr(outImage),-1)
new_image = nib.Nifti1Image(outImage,aff)
nib.save(new_image,fileNAME[:-7]+'_FASTseg_MICOS.nii.gz')
print 'time = '
print toc - tic
return (fileNAME[:-7]+'_FASTseg_MICOS.nii.gz')