SingleCellDetect

Detect a single cell located in the center of a 2d image

This package provided a method watershed_edge for detecting the boundaries of a single cell located at the center of an image and is useful when performing cell finding on biological images or movie. It may be used together with other packages that finds the locations of cell centers in a larger image.

install

pip install single_cell_detect

or

git clone https://github.com/sofroniewn/SingleCellDetect

example

import matplotlib.pyplot as plt
from single_cell_detect import watershed_edge as detect
from skimage.data import coins

image = coins()[:100,:100]
mask = detect(image, dilationSize=1, radial=True, filterSize=5)

fig, axes = plt.subplots(ncols=2, figsize=(6, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax0, ax1 = axes

ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title('Coins')
ax1.imshow(mask, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('Mask')

for ax in axes:
    ax.axis('off')

fig.subplots_adjust(hspace=0.01, wspace=0.01, top=0.9, bottom=0, left=0, right=1)
plt.show()

usage

mask = watershed_edge(image, dilationSize=0, radial=True, filterSize=0)

Returns a single cell mask found using a marker controlled watershed on the edges of image. The dilationSize controls the amount of dilation applied to the central maker for the marker controlled segmentation. If radial is true the edge detection will be done radially by first transforming image to polar coordinates. The filterSize controls the amount of median filtering applied after edge detection before the watershed. If radial is false the filtering is with a disk, if radial is true the filtering is tangent to the radial direction.

license

MIT