def _find_HU_slice(self):
"""Using a brute force search of the images, find the median HU linearity slice.
This method walks through all the images and takes a collapsed circle profile where the HU
linearity ROIs are. If the profile contains both low (<800) and high (>800) HU values and most values are the same
(i.e. its not an artifact, then
it can be assumed it is an HU linearity slice. The median of all applicable slices is the
center of the HU slice.
Returns
-------
int
The middle slice of the HU linearity module.
"""
hu_slices = []
for image_number in range(self.num_images):
image = self.images[:, :, image_number]
slice = Slice(self, Image.from_array(image))
try:
slice.find_phan_center()
except ValueError: # a slice without the phantom in view
continue
else:
circle_prof = CollapsedCircleProfile(slice.phan_center, radius=120/self.fov_ratio)
circle_prof.get_profile(image, width_ratio=0.05, num_profiles=5)
prof = circle_prof.y_values
# determine if the profile contains both low and high values and that most values are the same
if (np.percentile(prof, 2) < 800) and (np.percentile(prof, 98) > 800) and (np.percentile(prof, 80) - np.percentile(prof, 30) < 40):
hu_slices.append(image_number)
center_hu_slice = int(np.median(hu_slices))
return center_hu_slice
def __init__(self, settings):
super().__init__(settings)
self.scale_by_FOV()
self.image = Image.from_array(combine_surrounding_slices(self.settings.images, self.settings.SR_slice_num, mode='max'))
self.LP_MTF = OrderedDict() # holds lp:mtf data
for idx, radius in enumerate(self.radius2profs):
c = SR_Circle_ROI(idx, self.image.array, radius=radius)
self.add_ROI(c)
super().find_phan_center()
def __init__(self, settings):
super().__init__(settings)
self.scale_by_FOV()
self.image = Image.from_array(combine_surrounding_slices(self.settings.images, self.settings.UN_slice_num))
HU_ROIp = partial(HU_ROI, slice_array=self.image.array, tolerance=settings.hu_tolerance, radius=self.obj_radius,
dist_from_center=self.dist2objs)
# center has distance of 0, thus doesn't use partial
center = HU_ROI('Center', 0, 0, self.image.array, self.obj_radius, dist_from_center=0, tolerance=settings.hu_tolerance)
right = HU_ROIp('Right', 0, 0)
top = HU_ROIp('Top', -90, 0)
left = HU_ROIp('Left', 180, 0)
bottom = HU_ROIp('Bottom', 90, 0)
self.add_ROI(center, right, top, left, bottom)
super().find_phan_center()
def __init__(self, settings):
super().__init__(settings)
self.scale_by_FOV()
self.image = Image.from_array(combine_surrounding_slices(self.settings.images, self.settings.HU_slice_num))
HU_ROIp = partial(HU_ROI, slice_array=self.image.array, radius=self.object_radius, dist_from_center=self.dist2objs,
tolerance=settings.hu_tolerance)
air = HU_ROIp('Air', 90, -1000)
pmp = HU_ROIp('PMP', 120, -200)
ldpe = HU_ROIp('LDPE', 180, -100)
poly = HU_ROIp('Poly', -120, -35)
acrylic = HU_ROIp('Acrylic', -60, 120)
delrin = HU_ROIp('Delrin', 0, 340)
teflon = HU_ROIp('Teflon', 60, 990)
self.add_ROI(air, pmp, ldpe, poly, acrylic, delrin, teflon)
super().find_phan_center()
def test_open(self):
"""Test the open class method."""
# load a tif file
img = Image(img_path)
self.assertEqual(img.im_type, IMAGE)
# load a dicom file
img2 = Image(dcm_path)
self.assertEqual(img2.im_type, DICOM)
# try loading a bad file
bad_file = osp.abspath(__file__)
self.assertRaises(TypeError, Image, bad_file)
# not a valid parameter
bad_input = 3.5
self.assertRaises(TypeError, Image, bad_input)
# load an array
dcm = dicom.read_file(dcm_path)
img = Image.from_array(dcm.pixel_array)
self.assertEqual(img.im_type, ARRAY)
def __init__(self, settings):
super().__init__(settings)
self.tolerance = settings.scaling_tolerance
self.scale_by_FOV()
self.image = Image.from_array(combine_surrounding_slices(self.settings.images, self.settings.HU_slice_num, mode='median'))
GEO_ROIp = partial(GEO_ROI, slice_array=self.image.array, radius=self.obj_radius,
dist_from_center=self.dist2objs)
tl = GEO_ROIp(name='Top-Left', angle=-135)
tr = GEO_ROIp(name='Top-Right', angle=-45)
br = GEO_ROIp(name='Bottom-Right', angle=45)
bl = GEO_ROIp(name='Bottom-Left', angle=135)
self.add_ROI(tl, tr, br, bl)
# Construct the Lines, mapping to the nodes they connect to
lv = GEO_Line('Left-Vert', tl, bl)
bh = GEO_Line('Bottom-Horiz', bl, br)
rv = GEO_Line('Right-Vert', tr, br)
th = GEO_Line('Top-Horiz', tl, tr)
self.add_line(lv, bh, rv, th)
super().find_phan_center()
def __init__(self, settings):
super().__init__(settings)
self.image = Image.from_array(combine_surrounding_slices(self.settings.images, self.settings.LC_slice_num))
def setUp(self):
self.img = Image(img_path)
self.dcm = Image(dcm_path)
small_array = np.arange(42).reshape(6,7)
self.sm_arr = Image.from_array(small_array)
