Why this regionprops returns 0x1 struct?

Question

I am trying to convert the code of MIT\'s course Biological Instrumentation and Measurement in the wiki page here from Matlab 7.3 to Matlab R2016a. My input data\'

Answer 1

To my understanding your input image isn't a PSF image. Quoting from the link you provided, a PSF image is an image of approximate point sources on a dark background, such as a star field or sub resolution fluorescent microspheres. You can generate such image for testing using the SimulatePsfSlide function in the given code.

EDIT

I don't have Matlab. I ran the code in Octave with a simple PSF image having a single point source in the middle of the image generated from the code below. You can first try with a simple known image and check the result.

In the code below, you can vary the Gaussian PSF size and sigma and see how nlinfit estimates the sigma.

Over-exposure shouldn't be a problem, those values are clipped according to the test code in the link.

 clear all
 close all

 psfSize = 9;
 psfSigma = 5;

 % single point source in the middle: this is the object
 ImageSize = [500 500];
 im = im2double( zeros( ImageSize ) );
 im( int32(ImageSize(1)/2), int32(ImageSize(2)/2) ) = 1;
 % gaussian psf: this is the psf of our imaging system
 h = fspecial('gaussian', [psfSize psfSize], psfSigma);
 % convolve the object with psf: the image, this is what we see
 simulatedPsfImage = imfilter(im, h, 'same');
 simulatedPsfImage = im2double( simulatedPsfImage );
 % estimating resolution
 [ measuredResolution, standardError, bestFitData ] = ...
        EstimateResolutionFromPsfImage( simulatedPsfImage );

Input data and nlinfit output (beta and MSE only): Note that in the second case, the MSE is smaller, indicating that the input data closely matches the model. Also we get the correct sigma.

psfSize = 9, sigma = 5, estimated sigma = 3.0730

beta = 
2.5000e+002
2.5000e+002
2.0275e-002
3.0730e+000
-4.4688e-004

mse =   1.6114e-006

psfSize = 25, sigma = 5, estimated sigma = 5.0000

beta = 
2.5000e+002
2.5000e+002
6.5254e-003
5.0000e+000
7.3796e-010

mse =   2.2996e-020

Output in Matlab 2016a with psfSize=9 and psfSigma=5, which shows a significant difference between fspecial in Matlab and Octave

where bestFitData = 250.000000000593 250.000000000593 0.0202577533025840 3.07726724108174 -0.000451857701021258; here estimatedSigma = 3.077.