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61 lines
2.9 KiB
Matlab
61 lines
2.9 KiB
Matlab
% CROP_OUTLIERS in 2D binary slice identify the N largest structures and
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% remove all smallers
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%
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% mask_new = crop_outliers(mask, number_of_objects)
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%
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% Inputs
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% **mask original 2D binary mask
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% **number_of_objects Number of object to be left
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% *returns*
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% ++mask_new updated mask
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%*-----------------------------------------------------------------------*
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%| |
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%| Except where otherwise noted, this work is licensed under a |
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%| Creative Commons Attribution-NonCommercial-ShareAlike 4.0 |
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%| International (CC BY-NC-SA 4.0) license. |
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%| |
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%| Copyright (c) 2017 by Paul Scherrer Institute (http://www.psi.ch) |
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%| |
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%| Author: CXS group, PSI |
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%*-----------------------------------------------------------------------*
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% You may use this code with the following provisions:
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%
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% If the code is fully or partially redistributed, or rewritten in another
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% computing language this notice should be included in the redistribution.
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%
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% If this code, or subfunctions or parts of it, is used for research in a
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% publication or if it is fully or partially rewritten for another
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% computing language the authors and institution should be acknowledged
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% in written form in the publication: “Data processing was carried out
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% using the “cSAXS matlab package” developed by the CXS group,
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% Paul Scherrer Institut, Switzerland.”
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% Variations on the latter text can be incorporated upon discussion with
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% the CXS group if needed to more specifically reflect the use of the package
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% for the published work.
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%
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% A publication that focuses on describing features, or parameters, that
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% are already existing in the code should be first discussed with the
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% authors.
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%
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% This code and subroutines are part of a continuous development, they
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% are provided “as they are” without guarantees or liability on part
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% of PSI or the authors. It is the user responsibility to ensure its
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% proper use and the correctness of the results.
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function mask_new = crop_outliers(mask, number_of_objects)
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if nargin == 1
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number_of_objects = 1;
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end
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L0 = double(labelmatrix(bwconncomp(mask)));
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[m,n] = hist(L0(L0>0),unique(L0(L0>0)));
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[~,ind] = sort(m);
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try
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mask_new = ismember(L0, n(ind(max(1,end - number_of_objects+1):end)));
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catch
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keyboard
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end
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end
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