def apply(self, rois, dataset):
channel = sima.misc.resolve_channels(self._channel,
dataset.channel_names)
processed_im = _processed_image_ca1pc(dataset, channel,
self._x_diameter,
self._y_diameter)[0]
shape = processed_im.shape[:2]
ROIs = ROIList([])
for roi in rois:
roi_indices = np.nonzero(roi.mask[0])
roi_indices = np.ravel_multi_index(roi_indices, shape)
# pixel values in the cut
vals = processed_im.flat[roi_indices]
# indices of those values below the otsu threshold
# if all values are identical, continue without adding an ROI
try:
roi_indices = roi_indices[vals < threshold_otsu(vals)]
except ValueError:
continue
# apply binary opening and closing to the surviving pixels
# expand the shape by 1 in all directions to correct for edge
# effects of binary opening/closing
twoD_indices = [np.unravel_index(x, shape) for x in roi_indices]
mask = np.zeros([x + 2 for x in shape])
for indices in twoD_indices:
mask[indices[0] + 1, indices[1] + 1] = 1
mask = ndimage.binary_closing(ndimage.binary_opening(mask))
mask = mask[1:-1, 1:-1]
roi_indices = np.where(mask.flat)[0]
# label blobs in each cut
labeled_array, num_features = measurements.label(mask)
for feat in range(num_features):
blob_inds = np.where(labeled_array.flat == feat + 1)[0]
twoD_indices = [np.unravel_index(x, shape) for x in blob_inds]
mask = np.zeros(shape)
for x in twoD_indices:
mask[x] = 1
ROIs.append(ROI(mask=mask))
return ROIs
def apply(self, rois, dataset):
channel = sima.misc.resolve_channels(self._channel,
dataset.channel_names)
processed_im = _processed_image_ca1pc(
dataset, channel, self._x_diameter, self._y_diameter)[0]
shape = processed_im.shape[:2]
ROIs = ROIList([])
for roi in rois:
roi_indices = np.nonzero(roi.mask[0])
roi_indices = np.ravel_multi_index(roi_indices, shape)
# pixel values in the cut
vals = processed_im.flat[roi_indices]
# indices of those values below the otsu threshold
# if all values are identical, continue without adding an ROI
try:
roi_indices = roi_indices[vals < threshold_otsu(vals)]
except ValueError:
continue
# apply binary opening and closing to the surviving pixels
# expand the shape by 1 in all directions to correct for edge
# effects of binary opening/closing
twoD_indices = [np.unravel_index(x, shape) for x in roi_indices]
mask = np.zeros([x + 2 for x in shape])
for indices in twoD_indices:
mask[indices[0] + 1, indices[1] + 1] = 1
mask = ndimage.binary_closing(ndimage.binary_opening(mask))
mask = mask[1:-1, 1:-1]
roi_indices = np.where(mask.flat)[0]
# label blobs in each cut
labeled_array, num_features = measurements.label(mask)
for feat in range(num_features):
blob_inds = np.where(labeled_array.flat == feat + 1)[0]
twoD_indices = [np.unravel_index(x, shape) for x in blob_inds]
mask = np.zeros(shape)
for x in twoD_indices:
mask[x] = 1
ROIs.append(ROI(mask=mask))
return ROIs
def _rois_from_cuts_full(cuts):
"""Return ROI structures each containing the full extent of a cut.
Parameters
----------
cuts : list of sima.normcut.CutRegion
The segmented regions identified by normalized cuts.
Returns
-------
sima.ROI.ROIList
ROI structures corresponding to each cut.
"""
ROIs = ROIList([])
for cut in cuts:
if len(cut.indices):
mask = np.zeros(cut.shape)
for x in cut.indices:
mask[np.unravel_index(x, cut.shape)] = 1
ROIs.append(ROI(mask=mask))
return ROIs
def _rois_from_cuts(cls, cuts):
"""Return ROI structures each containing the full extent of a cut.
Parameters
----------
cuts : list of sima.normcut.CutRegion
The segmented regions identified by normalized cuts.
Returns
-------
sima.ROI.ROIList
ROI structures corresponding to each cut.
"""
ROIs = ROIList([])
for cut in cuts:
if len(cut.indices):
mask = np.zeros(cut.shape)
for x in cut.indices:
mask[np.unravel_index(x, cut.shape)] = 1
ROIs.append(ROI(mask=mask))
return ROIs
def _rois_from_cuts_ca1pc(cuts, im_set, circularity_threhold=0.5,
min_roi_size=20, min_cut_size=30,
channel=0, x_diameter=8, y_diameter=8):
"""Return ROI structures containing CA1 pyramidal cell somata.
Parameters
----------
cuts : list of sima.normcut.CutRegion
The segmented regions identified by normalized cuts.
circularity_threhold : float
ROIs with circularity below threshold are discarded. Default: 0.5.
min_roi_size : int, optional
ROIs with fewer than min_roi_size pixels are discarded. Default: 20.
min_cut_size : int, optional
No ROIs are made from cuts with fewer than min_cut_size pixels.
Default: 30.
channel : int, optional
The index of the channel to be used.
x_diameter : int, optional
The estimated x-diameter of the nuclei in pixels
y_diameter : int, optional
The estimated y_diameter of the nuclei in pixels
Returns
-------
sima.ROI.ROIList
ROI structures each corresponding to a CA1 pyramidal cell soma.
"""
processed_im = _processed_image_ca1pc(im_set, channel, x_diameter,
y_diameter)
shape = processed_im.shape[:2]
ROIs = ROIList([])
for cut in cuts:
if len(cut.indices) > min_cut_size:
# pixel values in the cut
vals = processed_im.flat[cut.indices]
# indices of those values below the otsu threshold
# if all values are identical, continue without adding an ROI
try:
roi_indices = cut.indices[vals < threshold_otsu(vals)]
except ValueError:
continue
# apply binary opening and closing to the surviving pixels
# expand the shape by 1 in all directions to correct for edge
# effects of binary opening/closing
twoD_indices = [np.unravel_index(x, shape) for x in roi_indices]
mask = np.zeros([x + 2 for x in shape])
for indices in twoD_indices:
mask[indices[0] + 1, indices[1] + 1] = 1
mask = ndimage.binary_closing(ndimage.binary_opening(mask))
mask = mask[1:-1, 1:-1]
roi_indices = np.where(mask.flat)[0]
# label blobs in each cut
labeled_array, num_features = label(mask)
for feat in range(num_features):
blob_inds = np.where(labeled_array.flat == feat + 1)[0]
# Apply min ROI size threshold
if len(blob_inds) > min_roi_size:
twoD_indices = [np.unravel_index(x, shape)
for x in blob_inds]
mask = np.zeros(shape)
for x in twoD_indices:
mask[x] = 1
# APPLY CIRCULARITY THRESHOLD
poly_pts = np.array(mask2poly(mask)[0].exterior.coords)
p = 0
for x in range(len(poly_pts) - 1):
p += np.linalg.norm(poly_pts[x] - poly_pts[x + 1])
shape_area = len(roi_indices)
circle_area = np.square(p) / (4 * np.pi)
if shape_area / circle_area > circularity_threhold:
ROIs.append(ROI(mask=mask))
return ROIs
