def watershed_mask(self, stain_thresh: Number, markers: np.ndarray,
disk_size: Optional[int]):
"""Create a watershed mask that is the union of the spot intensities above stain_thresh and
a marker image generated from nuclei
Parameters
----------
stain_thresh : Number
threshold to apply to the stain image
markers : np.ndarray[bool]
markers for the stain_image
disk_size : Optional[int]
if provided, execute a morphological opening operation over the thresholded stain image
Returns
-------
np.ndarray[bool] :
thresholded stain image
"""
st = self.stain >= stain_thresh
watershed_mask: np.ndarray = np.logical_or(st,
markers > 0) # dtype bool
if disk_size is not None:
watershed_mask = bin_open(watershed_mask, disk_size)
return watershed_mask
def filter_nuclei(self, nuclei_thresh: float, disk_size: Optional[int]) -> np.ndarray:
"""Threshold the nuclei image at nuclei_thresh.
Parameters
----------
nuclei_thresh : float
Threshold the nuclei image at this value
disk_size : int
if passed, execute a binary opening of the filtered image
Returns
-------
np.ndarray[bool] :
thresholded image
"""
nuclei_filt = bin_thresh(self.nuclei, nuclei_thresh)
if disk_size is not None:
nuclei_filt = bin_open(nuclei_filt, disk_size)
return nuclei_filt
def watershed_mask(self, stain_thresh, markers, disk_size):
st = self.stain >= stain_thresh
watershed_mask = np.logical_or(st, markers > 0)
if disk_size is not None:
watershed_mask = bin_open(watershed_mask, disk_size)
return watershed_mask
def filter_dapi(self, dapi_thresh, disk_size):
dapi_filt = bin_thresh(self.dapi, dapi_thresh)
if disk_size is not None:
dapi_filt = bin_open(dapi_filt, disk_size)
return dapi_filt
