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Briefly, a tumor-specific binary mask was generated to distinguish the cancer cells from surrounding stromal tissue by thresholding the pan-cytokeratin images.
A binary mask was generated and the centroid of the cell was calculated by the mean value of x- and y-coordinate of the pixels within the mask region.
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For each of the patterns, the binary mask is generated according to M ( k, m ) : = 1, y p > y 0 0, otherwise, (9).
For example, an early study [8] employs a factorial hidden Markov model (HMM) to model a speaker, and a binary mask is generated by comparing the two estimated sources.
A brain tissue mask was generated using a previously described automated skull-stripping algorithm (Badea et al. 2007) based on interactive thresholding and binary morphological operations.
This mask is generated by performing a kernel blur followed by a binary threshold on the source OCT structural image.
This selection mask is generated by a selection mask shader.
Once the pixelwise GMM likelihood is obtained, the final binary mask is either generated by thresholding [4, 6, 7] or according to more sophisticated decision rules [8 10].
Neuropil masks were generated in the JFRC2013 standard brain by alignment to the binary masks of the neuropils defined in Ito et al. (2014), followed by manual editing of the neuropil borders guided by the nc82 staining of the JFRC2013 brain.
Using these contours, tumor masks were generated.
Nuclear masks were generated from the DAPI channel using a watershed algorithm to segment the signals from background and morphological filters to transform the signals to coherent binary mask, covering the entire nuclear space.
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