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I have a 2d binary array of 0s and 1s. I select an element and would like to know how many 1s are in the contiguous cluster that includes the selected point. If the selected element is 0, the answer is 0. If the selected element is 1, then one should do a recursive search to find all nearest neighbors whose value = 1, and then their nearest neighbors with value =1, etc. Here, a nearest neighbor is by row- or column- but not both (no diagonal relations). There are 4 nearest neighbors to each interior point.

Logically, there would be binary image-processing operations that might help, especially ones on cluster analysis, but I don't see anything relevant and don't see how to do a recursive algorithm.

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  • $\begingroup$ A small example would really help. Anyway, have you tried anything on your own? Regarding recursion, of course you can write a recursive function in MMA in the obvious way (e.g. g[x_] := 2 g[x-1], together with appropriate starting values). You may also use Nest, Fold, etc. $\endgroup$ – MarcoB Jun 18 '18 at 1:36
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Using MorphologicalComponents with ComponentMeasurements:

ClearAll[counts]
counts[m_] := # /. ComponentMeasurements[#, "Count"] & @
  MorphologicalComponents[m, CornerNeighbors -> False]

Using Carl's example array:

SeedRandom[0]
m = RandomInteger[1, {10, 10}];
m // MatrixForm // TeXForm

$\left( \begin{array}{cccccccccc} 1 & 0 & 1 & 0 & 0 & 1 & 1 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 \\ 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 \\ 1 & 1 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 1 \\ 1 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 1 \\ 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 1 \\ 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 \\ 1 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 0 \\ \end{array} \right)$

counts[m][[3, 4]]

15

counts[m][[3, 2]]

0

counts @ m // MatrixForm // TeXForm

$ \left( \begin{array}{cccccccccc} 1 & 0 & 2 & 0 & 0 & 3 & 3 & 3 & 0 & 0 \\ 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 3 & 0 \\ 0 & 0 & 0 & 15 & 0 & 15 & 0 & 3 & 3 & 0 \\ 0 & 0 & 15 & 15 & 15 & 15 & 0 & 0 & 0 & 0 \\ 15 & 15 & 15 & 15 & 0 & 15 & 15 & 0 & 0 & 1 \\ 15 & 15 & 15 & 0 & 1 & 0 & 0 & 0 & 4 & 0 \\ 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 4 & 4 \\ 4 & 0 & 0 & 3 & 3 & 0 & 0 & 0 & 0 & 4 \\ 4 & 4 & 0 & 0 & 0 & 0 & 3 & 0 & 1 & 0 \\ 4 & 0 & 1 & 0 & 1 & 0 & 3 & 3 & 0 & 0 \\ \end{array} \right)$

cnts = Union[Join @@ counts[m]];

Row[{MatrixPlot[m, ImageSize -> 250,   
   ColorFunction  -> "Rainbow", ColorFunctionScaling -> False,
   PlotLegends -> SwatchLegend["Rainbow", {0, 1}] ], 
 MatrixPlot[counts @ m,  ImageSize -> 250, 
   ColorFunction -> ColorData[{"Rainbow", {0, Max[cnts]}}], 
   ColorFunctionScaling -> False, 
   PlotLegends -> SwatchLegend[ColorData["Rainbow"]/@Rescale[cnts], cnts] ]}, 
 Spacer[10]]

enter image description here

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  • $\begingroup$ These are great suggestions. Thanks so much to you both. (I had looked at MorphologicalComponents but could not quite see how to get it all to work.) $\endgroup$ – John Bechhoefer Jun 19 '18 at 1:53
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You could use MorphologicalComponents:

size[m_, pos_] := With[{comp = MorphologicalComponents[m, CornerNeighbors->False]},
    c = Extract[comp, pos];
    Replace[c, Except[0] :> Count[comp, c, 2]]
]

An example matrix:

SeedRandom[0]
m = RandomInteger[1, {10,10}];
MatrixPlot[m]

enter image description here

And the size of the blob containing row 5, column 1:

size[m, {5,1}]

15

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