Description This is a very simple function to find the local maximum in any dimensional array. As simple as it is it still gives nice results.

I use the imdilate() function as a maximum operation and then compare the data to the result.

The function receives three parameters: 

the data, a vector defining the minimum distance between peaks in each of the data dimensions. and a flag either to exclude equal points or not.

use examples: 

a = cumsum(randn(1000,1)); 

peaks = localMaximum(a,[100]); 

figure; plot(a); hold on; plot(peaks,a(peaks),'ro');

[x y] = meshgrid(-6:0.1:6,-6:0.1:6); 

a = sinc(x).*sinc(y); 

lMaxInd = localmaximum(a,[20 20]); 

lMinInd = localMaximum(-a,[20 20]); 

figure; mesh(x,y,a); hold on; 

plot3(x(lMaxInd),y(lMaxInd),a(lMaxInd),'k*','markersize',10,'linewidth',1.5); 

plot3(x(lMinInd),y(lMinInd),a(lMinInd),'g*','markersize',10','linewidth',1.5); 

legend('sinc(x)sinc(y)','peaks','valleys','location','best')

P.S 

- It is recommended to run (if possible) a LPF on the data before searching for the peaks

Required Products  Image Processing Toolbox

MATLAB release MATLAB 7.0.4 (R14SP2) 

function varargout = localMaximum(x,minDist, exculdeEqualPoints) % function varargout = localMaximum(x,minDist, exculdeEqualPoints) % % This function returns the indexessubscripts of local maximum in the data x. % x can be a vector or a matrix of any dimension % % minDist is the minimum distance between two peaks (local maxima) % minDist should be a vector in which each argument corresponds to it's % relevant dimension OR a number which is the minimum distance for all % dimensions % % exculdeEqualPoints - is a boolean definning either to recognize points with the same value as peaks or not % x = [1     2     3     4     4     4     4     4     4     3     3     3     2     1];   %  will the program return all the '4' as peaks or not -  defined by the 'exculdeEqualPoints' % localMaximum(x,3) % ans =  %      4     5     6     7     8     9    11    12 % %  localMaximum(x,3,true) % ans = %      4     7    12 %       % % Example: % a = randn(100,30,10); % minDist = [10 3 5]; % peaks = localMaximum(a,minDist); %  % To recieve the subscript instead of the index use: % [xIn yIn zIn] = localMaximum(a,minDist); % % To find local minimum call the function with minus the variable: % valleys = localMaximum(-a,minDist);
    if nargin < 3         exculdeEqualPoints = false;         if nargin < 2             minDist = size(x)/10;         end            end          if isempty(minDist)         minDist = size(x)/10;     end          dimX = length ( size(x) );     if length(minDist) ~= dimX         % In case minimum distance isn't defined for all of x dimensions         % I use the first value as the default for all of the dimensions         minDist = minDist( ones(dimX,1) );     end          % validity checks     minDist = ceil(minDist);     minDist = max( [minDist(:)' ; ones(1,length(minDist))] );     minDist = min( [minDist ; size(x)] );
    % ---------------------------------------------------------------------     if exculdeEqualPoints         % this section comes to solve the problem of a plato         % without this code, points with the same hight will be recognized as peaks         y = sort(x(:));         dY = diff(y);         % finding the minimum step in the data         minimumDiff = min( dY(dY ~= 0) );             adding noise which won't affect the peaks         x = x + rand(size(x))*minimumDiff;     end     % ---------------------------------------------------------------------               se = ones(minDist);     X = imdilate(x,se);     f = find(x == X);      
    if nargout         [varargout{1:nargout}] = ind2sub( size(x), f );     else         varargout{1} = f;

http://www.mathworks.com/matlabcentral/fileexchange/14498-local-maxima-minima