Scalable Cytometry Image Processing (SCIP) is an open-source tool that implements an image processing pipeline on top of Dask, a distributed computing framework written in Python. SCIP performs projection, illumination correction, image segmentation and masking, and feature extraction.
Currently, a mask is computed for every channel, which is used for computing features. If any of these channels contain multiple objects, the event is discarded. The bounding box is computed by taking the largest bounding box from all channels.
In the updated version, one channel is designated as the primary channel, which will be used for computing the bounding box. For feature extraction, one mask per channel is still computed. It should not be required that this is one connected component. This is no problem for texture and intensity features.
For shape features, regionprops returns one collection of props per separate region. The corresponding measurements for all regions have to be aggregated. For example, if for an event a stained channel has three components the regionprops will be computed for all three. We can then take the average of all three measurements.
Currently, a mask is computed for every channel, which is used for computing features. If any of these channels contain multiple objects, the event is discarded. The bounding box is computed by taking the largest bounding box from all channels.
In the updated version, one channel is designated as the primary channel, which will be used for computing the bounding box. For feature extraction, one mask per channel is still computed. It should not be required that this is one connected component. This is no problem for texture and intensity features.
For shape features, regionprops returns one collection of props per separate region. The corresponding measurements for all regions have to be aggregated. For example, if for an event a stained channel has three components the regionprops will be computed for all three. We can then take the average of all three measurements.