| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483 | function D=bwdistsc(bw,aspect)% D=BWDISTSC(BW,ASPECT)% BWDISTSC computes Euclidean distance transform of the binary 3D image % BW. For each pixel in BW, the distance transform assignes a number% that is the distance between that pixel and the nearest nonzero pixel % of BW. BW may be a single 2D image, 3D array or a cell array of % 2D slices. ASPECT is 3-component vector defining aspect ratio in % the dataset BW. If ASPECT is not specified, isotropic aspect % ratio [1 1 1] is assumed.%% BWDISTSC uses fast optimized scanning algorithm and cell-arrays to % represent internal data, so that it is less demanding to physical % memory. In many cases BWDISTSC is actually faster than MATLAB's % optimized kd-tree algorithm used for Euclidean distance % transform in 3D. %% BWDISTSC tries to use MATLAB bwdist for 2D scans if possible, which % is significantly faster. Otherwise BWDISTSC uses internal algorithm % to perform 2D scans.%%     Yuriy Mishchenko  JFRC HHMI Chklovskii Lab  JUL 2007% This code is free for use or modifications, just please give credit % where appropriate. And if you modify code or fix bugs, please drop % me a message at gmyuriy@hotmail.com.%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Scan algorithms below use following Lema:                     %% LEMA: let F(X,z) be lower envelope of a family of parabola:   %% F(X,z)=min_{i} [G_i(X)+(z-k_i)^2];                            %% and let H_k(X,z)=A(X)+(z-k)^2 be a parabola.                  %% Then for H_k(X,z)==F(X,z) at each X there exist at most       %% two solutions k1<k2 such that H_k12(X,z)=F(X,z), and          %% H_k(X,z)<F(X,z) is restricted to at most k1<k2.               %% Here X is any-dimensional coordinate.                         %%                                                               %% Thus, simply scan away from any z such that H_k(X,z)<F(X,z)   %% in either direction as long as H_k(X,z)<F(X,z) and update     %% F(X,z). Note that need to properly choose starting point;     %% starting point is any z such that H_k(X,z)<F(X,z); z==k is    %% usually, but not always the starting point!!!                 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% parse inputsif(nargin<2 || isempty(aspect)) aspect=[1 1 1]; end% determine geometry of dataif(iscell(bw)) shape=[size(bw{1}),length(bw)]; else shape=size(bw); end% fix to handle 1D & 2D imagesif(length(shape)<3) shape(length(shape)+1:3)=1; endif(length(aspect)<3) aspect(length(aspect)+1:3)=1; end% allocate spaceD=cell(1,shape(3)); for k=1:shape(3) D{k}=zeros(shape(1:2)); end%%%%%%%%%%%%% scan along XY %%%%%%%%%%%%%%%%for k=1:shape(3)        if(iscell(bw)) bwXY=bw{k}; else bwXY=bw(:,:,k); end            % initialize arrays    DXY=zeros(shape(1:2));    D1=zeros(shape(1:2));    DK=zeros(shape(1:2));    % if can, use 2D bwdist from image processing toolbox        if(exist('bwdist') && aspect(1)==aspect(2))        D1=aspect(1)^2*bwdist(bwXY).^2;    else    % if not, use full XY-scan        %%%%%%%%%%%%%%% X-SCAN %%%%%%%%%%%%%%%                % reference nearest bwXY "on"-pixel in x direction downward:                %  scan bottow-up, copy x-reference from previous row unless         %  there is bwXY "on"-pixel in that point in current row        xlower=repmat(Inf,shape(1:2));                 xlower(1,find(bwXY(1,:)))=1;    % fill in first row        for i=2:shape(1)            xlower(i,:)=xlower(i-1,:);  % copy previous row            xlower(i,find(bwXY(i,:)))=i;% unless there is pixel        end                % reference nearest bwXY "on"-pixel in x direction upward:        xupper=repmat(Inf,shape(1:2));                xupper(end,find(bwXY(end,:)))=shape(1);        for i=shape(1)-1:-1:1            xupper(i,:)=xupper(i+1,:);            xupper(i,find(bwXY(i,:)))=i;        end                        % find points for which distance needs to be updated        idx=find(~bwXY); [x,y]=ind2sub(shape(1:2),idx);                % set distance as the shortest to upward or to downward        DXY(idx)=aspect(1)^2*min((x-xlower(idx)).^2,(x-xupper(idx)).^2);                %%%%%%%%%%%%%%% Y-SCAN %%%%%%%%%%%%%%%        % this will be the envelop         % envelop is initialized at Inf to ensure single scan direction,         % otherwise may end up in infinite loop when trying to find         % starting point        D1=repmat(Inf,shape(1:2));        % these will be the references to parabolas defining the envelop        DK=repmat(Inf,shape(1:2));        % starting points        i0=zeros(shape(1),1);        % convenience x-coords array         x=(1:shape(1))';                 for i=1:shape(2)            % need to select starting point for each X:            % * starting point should be below current envelop            % * i0==i is not necessarily a starting point            % * there is at most one starting point            % * there may be no starting point                        % i0 is the starting points for each X: i0(X) is the first             % y-index such that parabola from line i is below the envelop                        % first guess is the current y-line            i0(:)=i;                        % some auxiliary datasets            d0=DXY(:,i);            % L0 indicates for which X starting point had been fixed            L0=isinf(d0) | (d0==0);                        while(~isempty(find(~L0,1)))                % reference starting points in DXY                idx=sub2ind(shape(1:2),x(~L0),i0(~L0));                                % reduce out trivial points (DXY==0)                L=(DXY(idx)==0);                L0(~L0)=L;                idx=idx(~L);                                if(isempty(idx)) continue; end                                % these are current best parabolas for starting points                ik=DK(idx);                                % these are new values from parabola from line #i                dtmp=d0(~L0)+aspect(2)^2*(i0(~L0)-i).^2;                                % these starting points are OK - below the envelop                L=D1(idx)>dtmp; D1(idx(L))=dtmp(L);                                % points which are still above the envelop but ik==i0,                % will not get any better, so fix them as well                L=L | (ik==i0(~L0));                                                % all other points are not OK, need new starting point:                % starting point should be at least below parabola                 % beating us at current choice of i0                                % solve quadratic equation to find where this happens                ik=(ik-i);                 di=(D1(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(2)^2;                % should select next highest index to the equality                di=fix(di)+sign(di);                                % the new starting points                idx=find(~L0);                 i0(idx(~L))=i0(idx(~L))+di;                % update L0 to indicate which points we've fixed                L0(~L0)=L; L0(idx(~L))=(di==0);                                % points that went out of boundaries can't get better;                 % fix them as well                idx=idx(~L);                idx=idx((i0(idx)<1) | (i0(idx)>shape(2)));                 i0(idx)=i;                L0(idx)=1;                            end            % reduce out trivial points DXY(idx)<DXY(:,i)            idx=sub2ind(shape(1:2),x,i0);            L=(DXY(idx)>0) | (i0==i);            idx=idx(L);            % these will keep track along which X should             % keep updating distances                        map_lower=L;            map_upper=L;                        idx_lower=idx;            idx_upper=idx;                        % set trivial pixels D==0 in line #i:            % this has to be done b/s we manually discarded them from L0            D1(d0==0,i)=0;            % scan from starting points for each X,i0 in increments of 1            di=0;       % distance from current y-line            eols=2;     % end-of-line-scan flag            totlen=prod(shape(1:2));            while(eols)                eols=2;                di=di+1;                                % select X which can be updated for di<0;                % i.e. X which had been below envelop all way till now                if(~isempty(idx_lower))                    % shift y by -1                    idx_lower=idx_lower-shape(1);                                        % prevent index dropping below 1st                    L=(idx_lower>=1);                    map_lower(map_lower)=L;                    idx_lower=idx_lower(L);                                        if(~isempty(idx_lower))                        dtmp=d0(map_lower)+...                            aspect(2)^2*(i0(map_lower)-di-i).^2;                                            % these pixels are to be updated with i0-di                        L=D1(idx_lower)>dtmp & DXY(idx_lower)>0;                        map_lower(map_lower)=L;                        idx_lower=idx_lower(L);                        D1(idx_lower)=dtmp(L);                        DK(idx_lower)=i;                    end                else    % if this is empty, get ready to quit                    eols=eols-1;                end                % select X which can be updated for di>0;                % i.e. X which had been below envelop all way till now                if(~isempty(idx_upper))                    % shift y by +1                    idx_upper=idx_upper+shape(1);                                        % prevent index from going over array limits                    L=(idx_upper<=totlen);                    map_upper(map_upper)=L;                    idx_upper=idx_upper(L);                                        if(~isempty(idx_upper))                                                dtmp=d0(map_upper)+...                            aspect(2)^2*(i0(map_upper)+di-i).^2;                                            % check which pixels are to be updated with i0+di                        L=D1(idx_upper)>dtmp & DXY(idx_upper)>0;                        map_upper(map_upper)=L;                        idx_upper=idx_upper(L);                        D1(idx_upper)=dtmp(L);                        DK(idx_upper)=i;                    end                else    % if this is empty, get ready to quit                    eols=eols-1;                end              end        end    end    D{k}=D1; end%%%%%%%%%%%%% scan along Z %%%%%%%%%%%%%%%%% this will be the envelop: % envelop has to be initialized at Inf to ensure single direction of scan, % otherwise may end up in infinite loop when trying to find starting pointD1=cell(size(D));for k=1:shape(3) D1{k}=repmat(Inf,shape(1:2)); end% these will be the references to parabolas defining the envelopDK=cell(size(D));for k=1:shape(3) DK{k}=repmat(Inf,shape(1:2)); end% start building the envelope for k=1:shape(3)    % need to select starting point for each X:    % * starting point should be below current envelop    % * k0==k is not necessarily a starting point    % * there may be no starting point        % k0 is the starting points for each XY: k0(XY) is the first    % z-index such that parabola from line k is below the envelop    % initial starting point guess is current slice    k0=repmat(k,shape(1:2));        % L0 indicates which starting points had been fixed    L0=isinf(D{k}) | (D{k}==0);    idxtot=find(~L0);        while(~isempty(idxtot))        % because of using cells need to explicitly scan in Z        % to avoid repetitious searches in k0, parse first        ss=getregions(k0(idxtot));        sslen=length(ss);                for kk=1:sslen            % these are starting points @kk which had not been set            idx=idxtot(ss(kk).PixelIdxList);                        % reduce out trivial points (D==0)            if(kk~=k)                L=(D{kk}(idx)==0);                L0(idx)=L;                idx=idx(~L);            end            if(isempty(idx)) continue; end                        % these are currently best parabolas for slice kk            ik=DK{kk}(idx);                                    % these are new values for slice kk from parabola from k            dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;                        % these points are OK - below current envelop            L=D1{kk}(idx)>dtmp; D1{kk}(idx(L))=dtmp(L);                                    % these points are not OK, but since ik==k0            % can't get any better            L=L | (ik==kk);                                        % all other points are not OK, need new starting point:            % starting point should be at least below parabola            % beating us at current choice of k0                        % solve quadratic equation to find where this happens            ik=(ik-k);            dk=(D1{kk}(idx(~L))-dtmp(~L))./ik(~L)/2/aspect(3)^2;            dk=fix(dk)+sign(dk);            k0(idx(~L))=k0(idx(~L))+dk;                % update starting points that had been set            L0(idx)=L;            L0(idx(~L))=(dk==0);                % points that went out of boundaries can't get better            idx=idx(~L);            idx=idx((k0(idx)<1) | (k0(idx)>shape(3)));            L0(idx)=1;            k0(idx)=k;        end        idxtot=find(~L0);    end        % map_lower/map_upper keeps track of which pixels can be yet updated    % with new distance, i.e. all such XY that had been below envelop for    % all dk up to now for dk<0/dk>0 respectively    map_lower=true(shape(1:2));    map_upper=true(shape(1:2));    % parse different values in k0 to avoid repetitious searching below    ss=getregions(k0);     sslen=length(ss);    % reduce out trivially faulty starting points    for kk=1:sslen        if(kk==k) continue; end                idx=ss(kk).PixelIdxList;                L=D{kk}(idx)>D{k}(idx);        map_lower(idx)=L;        map_upper(idx)=L;    end        % these are maintained to keep fast track of whether maps are empty    idx_lower=find(map_lower);    idx_upper=find(map_upper);        % set trivial pixels D==0 in slice k:    % this has to be done b/s we manually discarded them from L0    D1{k}(D{k}==0)=0;            % scan away from starting points in increments of 1    dk=0;       % distance from current xy-slice    eols=2;     % end-of-scan flag    while(eols)        eols=2;        dk=dk+1;        if(~isempty(idx_lower))            % prevent index from going over the boundaries            L=(k0(map_lower)-dk>=1);            map_lower(map_lower)=L;            % need to explicitly scan in Z because of using cell-arrays            for kk=1:sslen-dk                % get all XY such that k0-dk==kk                idx=ss(kk+dk).PixelIdxList;                 L=map_lower(idx);                idx=idx(L);                                if(~isempty(idx))                    dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;                                        % these pixels are to be updated with k0-dk                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;                    map_lower(idx)=L;                    D1{kk}(idx(L))=dtmp(L);                                        % ridiculously, but this is faster than                    % direct assignment                    dtmp=idx(L);                    dtmp(:)=k;                    DK{kk}(idx(L))=k;                end            end            idx_lower=idx_lower(map_lower(idx_lower));        else            eols=eols-1;        end        if(~isempty(idx_upper))            % prevent index from going over the boundaries                        L=(k0(map_upper)+dk<=shape(3));            map_upper(map_upper)=L;            % need to explicitly scan in Z because of using cell-arrays            for kk=dk+1:min(shape(3),sslen+dk)                % get all XY such that k0+dk==kk                idx=ss(kk-dk).PixelIdxList;                L=map_upper(idx);                idx=idx(L);                                if(~isempty(idx))                                        dtmp=D{k}(idx)+aspect(3)^2*(kk-k)^2;                        % these pixels are to be updated with k0+dk                    L=D1{kk}(idx)>dtmp & D{kk}(idx)>0;                    map_upper(idx)=L;                    D1{kk}(idx(L))=dtmp(L);                                        dtmp=idx(L);                     dtmp(:)=k;                    DK{kk}(idx(L))=dtmp;                end            end            idx_upper=idx_upper(map_upper(idx_upper));        else            eols=eols-1;        end    endend% the answerif(iscell(bw))    D=cell(size(bw));    for k=1:shape(3) D{k}=sqrt(D1{k}); endelse    D=zeros(shape);    for k=1:shape(3) D(:,:,k)=sqrt(D1{k}); endendfunction s=getregions(map)% this function is replacer for regionprops(map,'PixelIdxList');% it produces the list of different values along with list of % indexes of pixels in map with these values; 's' is struct-array % such that s(i).PixelIdxList contains list of pixels in map % with value i.% enable using regionprops if available, faster on 7.3fregionprops=1;% version control for using regionpropsv=version; v=str2num(v(1:3)); fregionprops=fregionprops & v>=7.3;% in later matlab regionprops is actually faster than this codeif(exist('regionprops') & fregionprops)    s=regionprops(map,'PixelIdxList');    returnendidx=(1:prod(size(map)))';dtmp=double(map(:));[dtmp,ind]=sort(dtmp); idx=idx(ind);ind=[0;find([diff(dtmp(:));1])];s=[];for i=2:length(ind)    if(dtmp(ind(i)))==0 continue; end    s(dtmp(ind(i))).PixelIdxList=idx(ind(i-1)+1:ind(i));end
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