def min_model(I,
Delta=0.3,
MaxLength=255,
Compaction=3,
MinArea=100,
MinWidth=5,
MinDepth=2,
MinConcavity=np.inf):
"""Performs a nuclear segmentation using a gradient contour tracing and
geometry splitting algorithm. Implemented from the reference below.
Parameters
----------
I : array_like
An intensity image used for analyzing local minima/maxima and
gradients. Dimensions M x N.
Delta : float
Fractional difference threshold between minima/maxima pairs to
be included in seed point detection. Fractional difference
([0, 1]) in total image range e.g. Delta = 0.3 with a uint8
input would translate to 0.3 * 255. Default value = 0.3.
MaxLength : int
Maximum allowable contour length. Default value = 255.
Compaction : int
Factor used in compacting objects to remove thin spurs. Refered to as
'd' in the paper. Default value = 3.
MinArea : int
Minimum area of objects to analyze. Default value = 100.
MinWidth : int
Minimum max-width of objects to analyze. Default value = 5.
MinDepth : float
Minimum depth of concavities to consider during geometric splitting.
Default value = 2.
MinConcavity : float
Minimum concavity score to consider when performing for geometric
splitting. Default value = np.inf.
Notes
-----
Objects are assumed to be dark (as nuclei in hematoxylin channel from color
deconvolution). Smoothing improves accuracy and computation time by
eliminating spurious seed points. Specifying a value for 'Delta' prevents
shallow transitions from being included, also reducing computation time and
increasing specificity.
Returns
-------
X : array_like
A 1D array of horizontal coordinates of contour seed pixels for
tracing.
Y : array_like
A 1D array of the vertical coordinates of seed pixels for tracing.
Min : array_like
A 1D array of the corresponding minimum values for contour tracing of
seed point X, Y.
Max : array_like
A 1D array of the corresponding maximum values for contour tracing of
seed point X, Y.
See Also
--------
histomicstk.segmentation.label.trace_object_boundaries
References
----------
.. [#] S. Weinert et al "Detection and Segmentation of Cell Nuclei in
Virtual Microscopy Images: A Minimum-Model Approach" in Nature
Scientific Reports,vol.2,no.503, doi:10.1038/srep00503, 2012.
"""
# identify contour seed points
X, Y, Min, Max = seed_contours(I, Delta)
# trace contours from seeds
cXs, cYs = trace_contours(I, X, Y, Min, Max, MaxLength=255)
# score successfully traced contours
Scores = score_contours(I, cXs, cYs)
# construct label image from scored contours
Label = label_contour(I.shape, cXs, cYs, Scores)
# compact contours to remove spurs - the paper calls this "optimization"
Label = label.compact(Label, Compaction)
# cleanup label image
Label = label.split(Label)
Label = label.area_open(Label, MinArea)
Label = label.width_open(Label, MinWidth)
# split objects with concavities
Label = split_concavities(Label, MinDepth, MinConcavity)
return Label
def min_model(I, Delta=0.3, MaxLength=255, Compaction=3,
MinArea=100, MinWidth=5, MinDepth=2, MinConcavity=np.inf):
"""Performs a nuclear segmentation using a gradient contour tracing and
geometry splitting algorithm. Implemented from the reference below.
Parameters
----------
I : array_like
An intensity image used for analyzing local minima/maxima and
gradients. Dimensions M x N.
Delta : float
Percent difference threshold between minima/maxima pairs to be included
in seed point detection. Percentage difference ([0, 1]) in total image
range e.g. Delta = 0.3 with a uint8 input would translate to 0.3 * 255.
Default value = 0.3.
MaxLength : int
Maximum allowable contour length. Default value = 255.
Compaction : int
Factor used in compacting objects to remove thin spurs. Refered to as
'd' in the paper. Default value = 3.
MinArea : int
Minimum area of objects to analyze. Default value = 100.
MinWidth : int
Minimum max-width of objects to analyze. Default value = 5.
MinDepth : float
Minimum depth of concavities to consider during geometric splitting.
Default value = 2.
MinConcavity : float
Minimum concavity score to consider when performing for geometric
splitting. Default value = np.inf.
Notes
-----
Objects are assumed to be dark (as nuclei in hematoxylin channel from color
deconvolution). Smoothing improves accuracy and computation time by
eliminating spurious seed points. Specifying a value for 'Delta' prevents
shallow transitions from being included, also reducing computation time and
increasing specificity.
Returns
-------
X : array_like
A 1D array of horizontal coordinates of contour seed pixels for
tracing.
Y : array_like
A 1D array of the vertical coordinates of seed pixels for tracing.
Min : array_like
A 1D array of the corresponding minimum values for contour tracing of
seed point X, Y.
Max : array_like
A 1D array of the corresponding maximum values for contour tracing of
seed point X, Y.
See Also
--------
histomicstk.segmentation.label.trace_boundaries
References
----------
.. [1] S. Weinert et al "Detection and Segmentation of Cell Nuclei in
Virtual Microscopy Images: A Minimum-Model Approach" in Nature
Scientific Reports,vol.2,no.503, doi:10.1038/srep00503, 2012.
"""
# identify contour seed points
X, Y, Min, Max = seed_contours(I, Delta)
# trace contours from seeds
cXs, cYs = trace_contours(I, X, Y, Min, Max, MaxLength=255)
# score successfully traced contours
Scores = score_contours(I, cXs, cYs)
# construct label image from scored contours
Label = label_contour(I.shape, cXs, cYs, Scores)
# compact contours to remove spurs - the paper calls this "optimization"
Label = label.compact(Label, Compaction)
# cleanup label image
Label = label.split(Label)
Label = label.area_open(Label, MinArea)
Label = label.width_open(Label, MinWidth)
# split objects with concavities
Label = split_concavities(Label, MinDepth, MinConcavity)
return Label