Algorithm for cluster-splitting a black and white image multiple images without white space

Question

I am attempting to write an algorithm to subdivide a black and white image into image clusters, that maximally omit white space. The practical use case is to do laser rastering more efficiently. (https://github.com/meerk40t/meerk40t/issues/500)

1. White edges of an image are omitted by the code already. Only white space between two black dots in a horizontal scanline is wasted.
2. Only black pixels need to be lasered.
3. By default the rastering process goes entirely across the image (except for white space at edges and entirely white scanlines).
4. There is some cost to the extra raster. 140ms per scanline, average rastering would be done around 150mm/s so performing two rasters with the same but shorter scanlines would need to remove about 2.1 cm per scanline to be worth subdividing the raster.
5. Overlapping images is fine so long as the black pixel is assigned to only one image.

For example if I have a black frame around white center. It would be highly efficient to cut the image into 4 rectangular parts with each rect covering one side of the frame.

Images which contain a lot of white space or consists of something like

... could be done much better doing 3 rasters; one for each ellipse. By default the raster will go across the entire width of the image.

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Is there a name for this algorithm with some solid theory behind it already? Is there an easy solution for this? While K-means clustering with each pixel being a element might work for the circles it seems like it would fail with regard to a frame. And standard euclidean distance is different than clustering into images.

Python using Pillow would be preferred but any algorithm that somewhat reasonably solves this problem would be greatly appreciated.