NoiseAnalysis.m - This script takes a series of images taken during a THL scan without any beam or source present (in a similar fashion to the script above). It records the THL value where each pixel sees 1, 10, 100… noise counts. Then, it plots a graph showing the no. of pixels with 1, 10, 100… noise counts at each THL value. This can be useful for deciding which pixels to mask when doing analysis of spectra.

ThresholdDistributions.m - After performing a threshold equalisation, there is a “save distributions” option which allows you to save the histograms produced during the equalisation. This script loads the histogram of threshold positions after equalisation, and does a Gaussian fit. This can, for example, allow you to find the position of the noise centroid, giving you the “zero” level of the detector after equalisation.

MeanImage.m - If you use Pixelman to take multiple acquisitions, an index will be added to the filename of each image. This is a very simple function to find the mean of the images, by looping through them.

FlatFieldCoefficients.m - After taking a series of flat field images and finding their mean, this generates an matrix containing flat field coefficients. Flat field correction can then be applied in MATLAB by multiplying an image by this matrix. Note that any pixels in the flat-field image containing zero counts cannot be corrected – this function sets the corresponding FF coefficients to 1.

FindMTFFromEdge_testbeam.m - This script takes an image of an edge, and finds the edge spread function from it. It then differentiates this to find the line spread function, and takes the Fourier transform to get the Modulation Transfer Function. (Insert reference.) There are also a few bits of code to extract data directly from the line spread function, such as the full-width half maximum. Ideally, the image of the edge should have been flat-field corrected.

ClusterAnalysis.m - If you use a short acquisition time or a lower-intensity source, so that most pixels have 0 hits and very few have more than 1, it becomes possible to distinguish between hits produced by alphas (large round clusters), betas (smaller, irregular clusters due to scattering) and gammas (photoelectric absorption, hitting only 1 or 2 pixels). See the image below. This example script uses histogramming and image erosion to distinguish between the different clusters. Example ASCII image files are available for a mixed alpha, beta, gamma source, and the same source with alphas removed using a slide.

Mixed source image:

MATLAB scripts for Medipix2 analysis

The Medipix2 USB interface produces output files that are just ASCII arrays containing the no. of photons detected by each pixel. These can be loaded into MATLAB and analysed.

-- DavidPennicard - 30 Jul 2008