[SciPy-User] low memory labeled component clustering

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[SciPy-User] low memory labeled component clustering

Edward Richards
I would like a LCC algorithm that produces sparse results. What I have
in mind would be similar to MATLAB's bwconncomp and return a sparse data
structure instead of a dense label array. I am new to image processing,
and have only a surface understanding of the LCC algorithm. I am hoping
that the experts out there can let me know if my goal is tenable (or
hopefully already implemented), before I dive too deeply into the
ndimage.label source code.

I am working on a detection algorithm, and the data is 3D and can be
very large in shape but sparse. I would like to process a bunch of data
at once to avoid edge effects. Currently I am maxing out my computers
RAM (32 GB) processing a (50, 200, 523264) element array. This array has
only ~3 million non-zero elements, or 0.00056 sparsity. For each cluster
I only need; the max value, it's location, and the number of elements.

Currently, the data is stored as a list of sparse matrices. Is it
possible to have the only 3D representation of the data be a Boolean
array that I pass to a clustering algorithm? For my problem, it is
better to take reasonable performance hits than hold temporary arrays in
memory.

Thank you,
Ned
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