Fast 2D peak finder

Find local maxima \ peak positions in noisy 2D arrays

Description

A simple and fast 2D peak finder. The aim was to be faster than more sophisticated techniques yet good enough to find peaks in noisy data to within 1 pixel accuracy. The code thresholds the data, median filters it, smooths it with a user defined filter, thresholds again, and looks for local maxima at relevant pixels. The code best works when using uint16 \ uint8 images, and assumes that peaks are relatively sparse. The code requires Matlab's Image Processing Toolbox. The code can be used inside parfor for faster processing times.

• Required Products: Image Processing Toolbox
• MATLAB release: MATLAB 7.13 (R2011b)

[Roman Voronov]

please add the option to get a 2d bw mask of the peaks, in addition to the awkward coordinate vector ...

[Sujay]

Thanks. it works well.

[natan]

@Sujay, as the code documentation say in line 8: "The 2D data raw image - assumes a Double\Single-precision ... or unit16 array. Please note that the code casts the raw image to uint16, if your image is 8-bit depth, I recommend to change all the places that uint16 is used to uint8 for faster run times." So, either use FastPeakFind(uint16(image),...) or change all uint16 to uint8 in the code. In future updates I'll include this feature automatically.

[Sujay]

I have a 512x512 .tif image that I can read in matlab as, A=imread(''). I am trying to use this program as,
FastPeakFind(A). It is showing the following error:
??? Error using ==> times
Integers can only be combined with integers of the same
class, or scalar doubles.

Error in ==> FastPeakFind at 81
d=d.*uint16(d>threshold);

What is wrong?

[Slawomir]

Great algorithm and pretty fast. Thanks !!!!

[natan]

@Carl Witthoft, Thanks for the comments, I've updated the information regarding the image processing toolbox requirement the scaling in case of a 0:1 range of image values. I've also already tested some more compact local maxima conditions, such as:
if all ( reshape( d(x(j),y(j)) >= d(x(j)-1:x(j)+1,y(j)-1:y(j)+1),9,1))

if d(x(j),y(j)) == max(max(d((x(j)-1):(x(j)+1),(y(j)-1):(y(j)+1))))

if d(x(j),y(j)) == max( reshape( d(x(j),y(j)) >= d(x(j)-1:x(j)+1,y(j)-1:y(j)+1),9,1))

All these were a factor of 2-3 slower than the simple 8-fold if condition. It takes Matlab more time to do the max or reshape functions than to go through the simple conditions...

Additionally, Matlab's image processing toolbox has the function imregionalmax, but I've found it is 6-7 times slower than this code.

[Carl Witthoft]

First commment lost.. try again. 1) Please specify that ImageProcessingToolbox is required. 2) For speed reasons, it may help to replace the 8-fold logical test with something like "d(j,j) > max(max(d(j-1:j+1,j-1:j+1))"

[Carl Witthoft]

One more thought: to allow for input files with float ranges (e.g. 0:1.0), replace the uint16 cast with "d = uint16( d.*2^16./(max(max(d))" or similar scaling function.

[Song]