def process(self, dataset, i):
print 'Register Data %s with mask...' % (self.ini.file.name_result[i])
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
1,
useMask=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
for j in range(3):
self.sheet.write(j + 1, i + 2, mean_dis[j])
self.sheet.write(4, i + 2, mean_whole)
self.book.save(self.path + self.ini.file.savedir + 'mask.xls')
del data, point, resultData
print 'Register Data %s without mask...' % (
self.ini.file.name_result[i])
data, point, para = self.icp.register(dataset['fix'], dataset['mov'],
1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
for j in range(3):
self.sheet.write(j + 5, i + 2, mean_dis[j])
self.sheet.write(8, i + 2, mean_whole)
self.book.save(self.path + self.ini.file.savedir + 'mask.xls')
del data, point, resultData
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[
i]
for sample in range(1, 11):
if dataset['mov'].getResolution()[-1] < 0.2:
times = 2
else:
times = 1
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'], 1, False, 0,
9999.0, sample * times)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Downsample %d Done!' % (sample)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.error[sample - 1, :3] += mean_dis
self.error[sample - 1, 3] += mean_whole
del data, point, resultData
def process(self, dataset, k, i):
self.sheet.write(0, k * 20 + i + 1, self.ini.file.name_result[i] + str(k))
print 'Register Data %s...' % self.ini.file.name_result[i]
if k == 0:
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 1, op = True)
elif k == 1:
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 1)
elif k == 2:
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 0, op = True)
else:
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 0)
resultData = db.ResultData(data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy())
self.sheet.write(1, k * 20 + i + 1, mean_whole)
mean_dis, mean_whole, max_dis, max_whole = self.contourareaerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy())
self.sheet.write(2, k * 20 + i + 1, mean_whole)
mean_dis, mean_whole, max_dis, max_whole = self.contourweigherror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy())
self.sheet.write(3, k * 20 + i + 1, mean_whole)
mean_dis, mean_whole, max_dis, max_whole = self.contourweigherror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy(), True)
self.sheet.write(4, k * 20 + i + 1, mean_whole)
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.book.save(self.path + self.ini.file.savedir + 'AllTest.xls')
del data, point, resultData, para
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[
i]
for sample in range(self.len):
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
0,
MaxRate=self.para[sample],
op=False)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Downsample %d Done!' % (sample)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.error[sample, 0] += mean_whole
self.error[sample, 1] += mean_whole**2
del data, point, resultData
def process(self, dataset, i, k):
print 'Register Data %s with ICP(centerline)...(Type %d)' % (
self.ini.file.name_result[i], k)
dataset['fix'].pointSet.data['Centerline'] = calCenterlineFromContour(
dataset['fix'].pointSet.data, k, dataset['fix'].getData())
dataset['mov'].pointSet.data['Centerline'] = calCenterlineFromContour(
dataset['mov'].pointSet.data, k, dataset['mov'].getData())
data, point, para = self.icp.register(dataset['fix'], dataset['mov'],
1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
for j in range(3):
self.sheet.write(j + 1 + 4 * k, i + 2, float(mean_dis[j]))
self.sheet.write(4 + 4 * k, i + 2, mean_whole)
self.book.save('./Result/Center_merge.xls')
del data, point, resultData
def load(self, dir):
result = []
for name in dir:
data, info, point = db.loadMatData(name, self.gui.dataModel)
if info.getData('fix') is not None:
fileData = db.ResultData(data, info, point)
else:
fileData = db.BasicData(data, info, point)
result.append(fileData)
return result
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[i]
for i in range(len(self.dis)):
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 1, w_wrong = self.dis[i]) # CLICP
#data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 0, False, delta) #SICP
resultData = db.ResultData(data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Weight %f Done!' % self.dis[i]
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy(), dataset['mov'].getPointSet('Contour').copy(), dataset['mov'].getPointSet('Mask').copy(), dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.error[i] += mean_whole
del data, point, resultData
def run(self, *args, **kwargs):
indexes = self.gui.getRegisterDataIndex()
if indexes:
if len(indexes) == 2:
data, point, para = self.register(
self.gui.dataModel[indexes[0]],
self.gui.dataModel[indexes[1]])
if data is None:
return
resultData = db.ResultData(
data,
db.ImageInfo(self.gui.dataModel[indexes[0]].info.data),
point)
resultData.addDetail('fix', indexes[0])
resultData.addDetail('move', indexes[1])
resultData.addDetail('transform', para)
resultData.setName(None)
return resultData
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[i]
for i in self.dis:
for j in self.dis:
data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 1, down_fix = i, down_mov = j, MaxRate = 1.0) # CLICP
#data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 0, False, delta) #SICP
#data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 1, False, delta, op = True) #CICP
#data, point, para = self.icp.register(dataset['fix'], dataset['mov'], 0, False, delta, op = True) #SLICP
resultData = db.ResultData(data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Sample (%d, %d) Done!' % (i, j)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy(), dataset['mov'].getPointSet('Contour').copy(), dataset['mov'].getPointSet('Mask').copy(), dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.error[i - 1, j - 1] += mean_whole
del data, point, resultData
def process(self, dataset, i):
'''
mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(dataset['seg'], dataset['fix'].getPointSet('Contour').copy())
print 'Segmentation Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.sheet1.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet1.write(j + 1, i + 2, mean_dis[j])
self.sheet1.write(4, i + 2, mean_whole)
self.book.save(self.path + self.ini.file.savedir + 'Test_segmentation.xls')
'''
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['seg'], dataset['mov'],
1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getResolution().tolist())
#mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
#dice_index, dice_index_all = self.areaerror.analysis(resultData, dataset['fix'].getPointSet('Contour').copy())
#print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet2.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet2.write(j + 1, i + 2, mean_dis[j])
self.sheet2.write(j + 5, i + 2, max_dis[j])
#self.sheet2.write(j + 9, i + 2, dice_index[j])
self.sheet2.write(4, i + 2, mean_whole)
self.sheet2.write(8, i + 2, max_whole)
#self.sheet2.write(12, i + 2, dice_index_all)
self.book.save(self.path + self.ini.file.savedir +
'Test_segmentation_surface.xls')
del data, point, resultData
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[
i]
for fov in range(7):
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'], 1, False, 0,
9999.0, 1, fov + 1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'FOV %dmm Done!' % (fov + 1)
mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.error[fov, :3] += mean_dis
self.error[fov, 3] += mean_whole
del data, point, resultData
def process(self, dataset, i):
print 'Register Data %s with ICP(centerline) with label...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['fix'], dataset['mov'],
1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
print 'Evaluation Data %s...' % self.ini.file.name_result[i]
mean_dis, mean_whole, max_dis, max_whole, result = self.contourerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(), True)
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
print result
for cnt in range(3):
for x in result[cnt].keys():
self.result[cnt][x] = self.result[cnt].get(x,
0) + result[cnt][x]
self.resultCnt[cnt][x] = self.resultCnt[cnt].get(x, 0) + 1
def process(self, dataset, i):
def autoDetectContour(point, cnt, start, end, delta, res, type):
self.new_points = npy.append(self.new_points, point, 0)
points = point[:, :-2]
d = 20
count = 0
for i in range(start + delta, end + delta, delta):
center = calCentroidFromContour(points).reshape(2)
image = dataset[type].getData()[i, :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) -
npy.min(image)) * 255
down = npy.max([npy.ceil(center[0] - d / res[0]), 0])
up = npy.min(
[npy.floor(center[0] + d / res[0]), image.shape[0]])
left = npy.max([npy.ceil(center[1] - d / res[1]), 0])
right = npy.min(
[npy.floor(center[1] + d / res[1]), image.shape[1]])
crop_image = image[down:up, left:right]
center -= [down, left]
result = ac_segmentation(center, crop_image)
a1 = ac_area(points.transpose(), image.shape)
a2 = ac_area(result, crop_image.shape)
rate = a2 * 1.0 / a1
if rate >= min(1.5 + count * 0.2, 2.1) or rate <= 0.7:
temp_array = points.copy()
if cnt != 1 and rate > 0.7:
count += 1
else:
temp_array = result.transpose().copy()
temp_array[:, :2] += [down, left]
count = 0
points = temp_array.copy()
temp_array = npy.insert(temp_array, 2, [[i], [cnt]], 1)
self.new_points = npy.append(self.new_points, temp_array, 0)
sys.stdout.write(str(i) + ',')
sys.stdout.flush()
print ' '
# Segmentation of data
print 'Segment Data %s...' % self.ini.file.name_result[i]
tmp = dataset['fix'].pointSet.data['Contour']
tmp = tmp[tmp[:, 0] >= 0]
self.new_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
bottom = int(npy.round(npy.min(tmp[:, 2])))
bif = int(db.getBifurcation(tmp) + 0.5)
up = int(npy.round(npy.max(tmp[:, 2])))
bottom += (bif - bottom) / 2
up -= (up - bif) / 2
point_vital = [0] * 3
point_vital[0] = tmp[(npy.round(tmp[:, -1]) == 0)
& (npy.round(tmp[:, 2]) == bottom)].copy()
point_vital[1] = tmp[(npy.round(tmp[:, -1]) == 1)
& (npy.round(tmp[:, 2]) == up)].copy()
point_vital[2] = tmp[(npy.round(tmp[:, -1]) == 2)
& (npy.round(tmp[:, 2]) == up)].copy()
autoDetectContour(point_vital[0], 0, bottom, bif, 1,
dataset['fix'].getResolution().tolist(), 'fix')
autoDetectContour(point_vital[1], 1, up, bif, -1,
dataset['fix'].getResolution().tolist(), 'fix')
autoDetectContour(point_vital[2], 2, up, bif, -1,
dataset['fix'].getResolution().tolist(), 'fix')
print ' '
print 'Finish segmentation for fix data. '
pointset = {'Contour': self.new_points}
dataset['fix'].pointSet.data['Centerline'] = calCenterlineFromContour(
pointset)
self.new_points_fix = self.new_points.copy()
# For mov data
tmp = dataset['mov'].pointSet.data['Contour']
tmp = tmp[tmp[:, 0] >= 0]
self.new_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
bottom = int(npy.round(npy.min(tmp[:, 2])))
bif = int(db.getBifurcation(tmp) + 0.5)
up = int(npy.round(npy.max(tmp[:, 2])))
bottom += (bif - bottom) / 2
up -= (up - bif) / 2
point_vital = [0] * 3
point_vital[0] = tmp[(npy.round(tmp[:, -1]) == 0)
& (npy.round(tmp[:, 2]) == bottom)].copy()
point_vital[1] = tmp[(npy.round(tmp[:, -1]) == 1)
& (npy.round(tmp[:, 2]) == up)].copy()
point_vital[2] = tmp[(npy.round(tmp[:, -1]) == 2)
& (npy.round(tmp[:, 2]) == up)].copy()
autoDetectContour(point_vital[0], 0, bottom, bif, 1,
dataset['mov'].getResolution().tolist(), 'mov')
autoDetectContour(point_vital[1], 1, up, bif, -1,
dataset['mov'].getResolution().tolist(), 'mov')
autoDetectContour(point_vital[2], 2, up, bif, -1,
dataset['mov'].getResolution().tolist(), 'mov')
print ' '
print 'Finish segmentation for mov data. '
pointset = {'Contour': self.new_points}
dataset['mov'].pointSet.data['Centerline'] = calCenterlineFromContour(
pointset)
self.new_points_mov = self.new_points.copy()
# ICP with centerline without label
print 'Register Data %s with ICP(centerline) without label...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
1,
op=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet2.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet2.write(j + 1, i + 2, mean_dis[j])
self.sheet2.write(j + 5, i + 2, max_dis[j])
self.sheet2.write(j + 9, i + 2, dice_index[j])
self.sheet2.write(4, i + 2, mean_whole)
self.sheet2.write(8, i + 2, max_whole)
self.sheet2.write(12, i + 2, dice_index_all)
self.book.save(self.path + self.ini.file.savedir +
'multicontrast_seg_feature.xls')
del data, point, resultData
# ICP with centerline with label
print 'Register Data %s with ICP(centerline) with label...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
1,
op=False)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet4.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet4.write(j + 1, i + 2, mean_dis[j])
self.sheet4.write(j + 5, i + 2, max_dis[j])
self.sheet4.write(j + 9, i + 2, dice_index[j])
self.sheet4.write(4, i + 2, mean_whole)
self.sheet4.write(8, i + 2, max_whole)
self.sheet4.write(12, i + 2, dice_index_all)
self.book.save(self.path + self.ini.file.savedir +
'multicontrast_seg_feature.xls')
del data, point, resultData
fix_points = dataset['fix'].getPointSet('Contour').copy()
dataset['fix'].pointSet.data['Contour'] = self.new_points_fix
mov_points = dataset['mov'].getPointSet('Contour').copy()
dataset['mov'].pointSet.data['Contour'] = self.new_points_mov
print 'Saving Data %s...' % self.ini.file.name_result[i]
db.saveMatData(
self.path + self.ini.file.savedir + self.ini.file.name_result[i] +
'_snap.mat', [dataset['fix']], 0)
db.saveMatData(
self.path + self.ini.file.savedir + self.ini.file.name_result[i] +
'_merge.mat', [dataset['mov']], 0)
print 'Done!'
# ICP with contour without label
print 'Register Data %s with ICP(contour) without label...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
0,
op=False)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, fix_points.copy(), mov_points.copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
para = resultData.info.getData('transform')
R = ml.mat(para[:9]).reshape(3, 3)
T = ml.mat(para[9:12]).T
T = R.I * T
T = -T
tmp_con, result_center_points = util.resliceTheResultPoints(
mov_points, None, 20, dataset['mov'].getResolution().tolist(),
dataset['fix'].getResolution().tolist(), False, R, T)
resultData.pointSet.data['Contour'] = tmp_con
dice_index, dice_index_all = self.areaerror.analysis(
resultData, fix_points.copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet1.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet1.write(j + 1, i + 2, mean_dis[j])
self.sheet1.write(j + 5, i + 2, max_dis[j])
self.sheet1.write(j + 9, i + 2, dice_index[j])
self.sheet1.write(4, i + 2, mean_whole)
self.sheet1.write(8, i + 2, max_whole)
self.sheet1.write(12, i + 2, dice_index_all)
self.book.save(self.path + self.ini.file.savedir +
'multicontrast_seg_feature.xls')
del data, point, resultData
# ICP with contour with label
print 'Register Data %s with ICP(contour) with label...' % self.ini.file.name_result[
i]
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
0,
op=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, fix_points.copy(), mov_points.copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
para = resultData.info.getData('transform')
R = ml.mat(para[:9]).reshape(3, 3)
T = ml.mat(para[9:12]).T
T = R.I * T
T = -T
tmp_con, result_center_points = util.resliceTheResultPoints(
mov_points, None, 20, dataset['mov'].getResolution().tolist(),
dataset['fix'].getResolution().tolist(), False, R, T)
resultData.pointSet.data['Contour'] = tmp_con
dice_index, dice_index_all = self.areaerror.analysis(
resultData, fix_points.copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet3.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet3.write(j + 1, i + 2, mean_dis[j])
self.sheet3.write(j + 5, i + 2, max_dis[j])
self.sheet3.write(j + 9, i + 2, dice_index[j])
self.sheet3.write(4, i + 2, mean_whole)
self.sheet3.write(8, i + 2, max_whole)
self.sheet3.write(12, i + 2, dice_index_all)
self.book.save(self.path + self.ini.file.savedir +
'multicontrast_seg_feature.xls')
del data, point, resultData
del self.new_points, fix_points, self.new_points_fix, self.new_points_mov, mov_points, tmp_con, result_center_points
def process(self, dataset, i):
# ICP with centerline
print 'Register Data %s with ICP...' % self.ini.file.name_result[i]
tmp = dataset['mov'].pointSet.data['Contour'].copy()
for sd in range(0, 16):
mean_dis_all = npy.zeros([4, 3], dtype=npy.float32)
mean_whole_all = npy.zeros([4, 1], dtype=npy.float32)
if sd > 0:
repeat = self.repeat
else:
repeat = 1
for i in range(repeat):
dataset['mov'].pointSet.data['Contour'] = AddNoise(
tmp,
float(sd) / 5)
dataset['mov'].pointSet.data[
'Centerline'] = calCenterlineFromContour(
dataset['mov'].pointSet.data)
# Centerline label
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'], 1)
resultData = db.ResultData(
data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
mean_dis_all[0, :] += mean_dis
mean_whole_all[0] += mean_whole
del data, point, resultData, para
# Contour label
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
0,
op=True)
resultData = db.ResultData(
data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
mean_dis_all[1, :] += mean_dis
mean_whole_all[1] += mean_whole
del data, point, resultData, para
# Centerline no-label
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'],
1,
op=True)
resultData = db.ResultData(
data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
mean_dis_all[2, :] += mean_dis
mean_whole_all[2] += mean_whole
del data, point, resultData, para
# Contour no-label
data, point, para = self.icp.register(dataset['fix'],
dataset['mov'], 0)
resultData = db.ResultData(
data, db.ImageInfo(dataset['fix'].info.data), point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
mean_dis_all[3, :] += mean_dis
mean_whole_all[3] += mean_whole
del data, point, resultData, para
sys.stdout.write(str(i) + ',')
sys.stdout.flush()
mean_dis_all /= repeat
mean_whole_all /= repeat
print ' '
print 'Noise level %fmm Done!' % (float(sd) / 5)
print 'Contour Error Done! Whole mean is %0.2fmm vs %0.2fmm.' % (
mean_whole_all[0], mean_whole_all[1])
for i in range(4):
self.error[i, sd, :3] += mean_dis_all[i, :]
self.error[i, sd, 3] += mean_whole_all[i]
def process(self, dataset, i):
# ICP with contour without label
print 'Register Data %s with ICP(contour) without label...' % self.ini.file.name_result[
i]
data, point, para, time = self.icp.register(dataset['fix'],
dataset['mov'],
0,
op=False,
isTime=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet1.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet1.write(j + 1, i + 2, mean_dis[j])
self.sheet1.write(j + 5, i + 2, max_dis[j])
self.sheet1.write(j + 9, i + 2, dice_index[j])
self.sheet1.write(4, i + 2, mean_whole)
self.sheet1.write(8, i + 2, max_whole)
self.sheet1.write(12, i + 2, dice_index_all)
self.sheet1.write(13, i + 2, time)
self.book.save(self.path + self.ini.file.savedir + 'snap_feature.xls')
del data, point, resultData
# ICP with centerline without label
print 'Register Data %s with ICP(centerline) without label...' % self.ini.file.name_result[
i]
data, point, para, time = self.icp.register(dataset['fix'],
dataset['mov'],
1,
op=True,
isTime=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet2.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet2.write(j + 1, i + 2, mean_dis[j])
self.sheet2.write(j + 5, i + 2, max_dis[j])
self.sheet2.write(j + 9, i + 2, dice_index[j])
self.sheet2.write(4, i + 2, mean_whole)
self.sheet2.write(8, i + 2, max_whole)
self.sheet2.write(12, i + 2, dice_index_all)
self.sheet2.write(13, i + 2, time)
self.book.save(self.path + self.ini.file.savedir + 'snap_feature.xls')
del data, point, resultData
# ICP with contour with label
print 'Register Data %s with ICP(contour) with label...' % self.ini.file.name_result[
i]
data, point, para, time = self.icp.register(dataset['fix'],
dataset['mov'],
0,
op=True,
isTime=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet3.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet3.write(j + 1, i + 2, mean_dis[j])
self.sheet3.write(j + 5, i + 2, max_dis[j])
self.sheet3.write(j + 9, i + 2, dice_index[j])
self.sheet3.write(4, i + 2, mean_whole)
self.sheet3.write(8, i + 2, max_whole)
self.sheet3.write(12, i + 2, dice_index_all)
self.sheet3.write(13, i + 2, time)
self.book.save(self.path + self.ini.file.savedir + 'snap_feature.xls')
del data, point, resultData
# ICP with centerline with label
print 'Register Data %s with ICP(centerline) with label...' % self.ini.file.name_result[
i]
data, point, para, time = self.icp.register(dataset['fix'],
dataset['mov'],
1,
op=False,
isTime=True)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.surfaceerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Contour').copy(),
dataset['mov'].getPointSet('Mask').copy(),
dataset['mov'].getResolution().tolist())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet4.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet4.write(j + 1, i + 2, mean_dis[j])
self.sheet4.write(j + 5, i + 2, max_dis[j])
self.sheet4.write(j + 9, i + 2, dice_index[j])
self.sheet4.write(4, i + 2, mean_whole)
self.sheet4.write(8, i + 2, max_whole)
self.sheet4.write(12, i + 2, dice_index_all)
self.sheet4.write(13, i + 2, time)
self.book.save(self.path + self.ini.file.savedir + 'time_feature.xls')
del data, point, resultData
def process(self, dataset, i):
def autoDetectContour(point, cnt, start, end, delta):
self.new_points = npy.append(self.new_points, point, 0)
points = point[:, :-2]
count = 0
for j in range(start + delta, end + delta, delta):
center = calCentroidFromContour(points).reshape(2)
image = dataset['fix'].getData()[j, :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) -
npy.min(image)) * 255
result = ac_segmentation(center, image)
a1 = ac_area(points.transpose(), image.shape)
a2 = ac_area(result, image.shape)
rate = a2 * 1.0 / a1
if rate >= min(1.5 + count * 0.2, 2.1) or rate <= 0.7:
temp_array = points.copy()
if cnt != 1 and rate > 0.7:
count += 1
else:
temp_array = result.transpose().copy()
count = 0
points = temp_array.copy()
temp_array = npy.insert(temp_array, 2, [[j], [cnt]], 1)
self.new_points = npy.append(self.new_points, temp_array, 0)
sys.stdout.write(str(j) + ',')
sys.stdout.flush()
print ' '
print 'Segment Data %s...' % self.ini.file.name_result[i]
tmp = dataset['fix'].pointSet.data['Contour']
tmp = tmp[tmp[:, 0] >= 0]
self.new_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
time1 = time.time()
bottom = int(npy.round(npy.min(tmp[:, 2])))
bif = int(db.getBifurcation(tmp) + 0.5)
up = int(npy.round(npy.max(tmp[:, 2])))
point_vital = [0] * 3
point_vital[0] = tmp[(npy.round(tmp[:, -1]) == 0)
& (npy.round(tmp[:, 2]) == bottom)].copy()
point_vital[1] = tmp[(npy.round(tmp[:, -1]) == 1)
& (npy.round(tmp[:, 2]) == up)].copy()
point_vital[2] = tmp[(npy.round(tmp[:, -1]) == 2)
& (npy.round(tmp[:, 2]) == up)].copy()
autoDetectContour(point_vital[0], 0, bottom, bif, 1)
autoDetectContour(point_vital[1], 1, up, bif, -1)
autoDetectContour(point_vital[2], 2, up, bif, -1)
time2 = time.time()
'''
# Use centerline for contour
j = 0
for center in tmp:
image = dataset['fix'].getData()[npy.round(center[2]), :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) - npy.min(image)) * 255
result = ac_segmentation(center[:2], image)
point_array = npy.insert(result.transpose(), 2, [[center[2]],[center[3]]], axis = 1)
new_points = npy.append(new_points, point_array, 0)
j += 1
if j % 10 == 0:
sys.stdout.write(str(j) + ',')
sys.stdout.flush()
'''
print ' '
print 'Done! Time for segmentation is %0.2fs' % (time2 - time1)
pointset = {'Contour': self.new_points}
pointset['Centerline'] = calCenterlineFromContour(pointset)
print 'Saving Data %s...' % self.ini.file.name_result[i]
new_data = db.BasicData(dataset['fix'].data,
db.ImageInfo(dataset['fix'].info.data),
pointset)
db.saveMatData(
self.savepath + self.ini.file.name_result[i] + '_mr.mat',
[new_data], 0)
print 'Done!'
mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(
new_data, dataset['fix'].getPointSet('Contour').copy())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
self.sheet1.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet1.write(j + 1, i + 2, mean_dis[j])
self.sheet1.write(4, i + 2, mean_whole)
self.sheet1.write(5, i + 2, time2 - time1)
self.book.save(self.path + self.ini.file.savedir +
'Test_segmentation_final_refined.xls')
# ICP with centerline
print 'Register Data %s with ICP(centerline)...' % self.ini.file.name_result[
i]
time1 = time.time()
data, point, para = self.icp.register(new_data, dataset['mov'], 1)
time2 = time.time()
print 'Done! Time for registration is %0.2fs' % (time2 - time1)
resultData = db.ResultData(data,
db.ImageInfo(dataset['fix'].info.data),
point)
resultData.info.addData('fix', 1)
resultData.info.addData('move', 2)
resultData.info.addData('transform', para)
mean_dis, mean_whole, max_dis, max_whole = self.contourerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Contour Error Done! Whole mean is %0.2fmm.' % mean_whole
dice_index, dice_index_all = self.areaerror.analysis(
resultData, dataset['fix'].getPointSet('Contour').copy())
print 'Area Error Done! Whole Dice index is %0.3f.' % dice_index_all
self.sheet2.write(0, i + 2, self.ini.file.name_result[i])
for j in range(3):
self.sheet2.write(j + 1, i + 2, mean_dis[j])
self.sheet2.write(j + 5, i + 2, max_dis[j])
self.sheet2.write(j + 9, i + 2, dice_index[j])
self.sheet2.write(4, i + 2, mean_whole)
self.sheet2.write(8, i + 2, max_whole)
self.sheet2.write(12, i + 2, dice_index_all)
self.sheet2.write(13, i + 2, time2 - time1)
self.book.save(self.path + self.ini.file.savedir +
'Test_segmentation_final_refined2.xls')
del data, point, resultData
