Handbook of Particle Detection and Imaging

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This shoud facilitate a better decision on a suitable filter for your processing purpose. How to: Choose a filter method from the drop down menu, key in a starting and an end radius. The image will be filtered in individual integer steps between the start and stop radius and presented in an image stack containing all the filtered images.

The filter can also be applied to only a ROI. This is recommended for filters which are cost intensive, like the "Gaussian Weighted Median". Purpose: The macro enables to "subtract" background due to inequal lighting from grayscale and true color images.

For true color images this is done using the brightness channel of an HSB stack. The original image brightness channel for true color images is divided by the flat-field image and the brightness is normalized using the mean intensity of the original image. Therefore, rather big radii sigma are needed potentially between , but this depends on image and feature size.

Advantage of the Pseudo flat field correction: This is now recordable and works with stacks.

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Thus, time-lapse movies e. The blurring is visualized on the currently active frame to be able to sufficiently eliminate structural information. Purpose: This tool enables to subtract the background from an image by creating a convoluted copy of the original image and subtracting the filtered image from it. This background subtraction method should facilitate consecutive feature extraction and is not suitable prior to intensity analyses!

How to: The user can choose between Gaussian, Median and Mean convolution filters and key in the respective filter radius or sigma for the Gaussian Blur. The preview option directly gives a possiblity to compare the results of the background subtraction. The radius for the Gaussian method can be chosen around the biggest feature diameter as for the rolling ball method.

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The median and mean methos might need bigger values to avoid elimination of bigger features! Method: The convoluted images are directly subtracted from the original with exception of the median filtered one. The latter additionally receives a grayscale dilation by application of a maximum filter with the factor 1. This should reduce artifacts around object borders. This enables to create plots which can be overlayed by choosing "add to existing plot".

In the case of a rectangular selection it can be chosen if the plotting direction should be horizontal or vertical. The intensities along the other direction are then averaged. Additionally, the color and look of the plot line can be chosen. This should enable to better compare intensity plots from different images or selections which is only possible if they have the same scaling. The latter is done in unscaled units pixels. If a new plot line is added to an existing plot the choice "Draw grid lines" is either ignored or forced depending on how the destination plot was created using the same tool.

How To: The line can be either straight, freehand or segmented and needs to be drawn beforehand. If the input image is a hyperstack the user can choose to plot over the z-slice or the t-frame range. In such a case the intensities of the active channel are taken for the plot.

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The macro automatically creates a stack of plots along the line selection with the upper intensity limit set at the highest intensity occurring along the line over all images. The filter Radius defines the size of a square kernel so actually not really a radius but to keep the entries intuitively similar to other filters in Fiji this label was chosen. The Shape option enables a basic pre-selection of pixels from the kernel neighborhood to be taken into account for filtering.

Handbook of Particle Detection and Imaging | SpringerLink

After pressing 'Ok', a checkbox grid will be displayed from which the user can adjust the selected pixels for the final filter according to the filtering needs e. Tolerance sets a threshold which will change intensity values after filtering only for those pixels where the difference to the original intensity is at least as high as the tolerance 0.

This enables to remove extreme outliers from the image while preserving the original pixel values in image areas without such outliers at least for shot noise. In the image below the upper pannels show the original photograph and a version with artificial shot noise added. Output: The filter will be applied directly on the input image.

It is undoable by pressing [z]. Suggestions are welcome! The previously filtered image will then be taken as basis for the next image filtering. The maximum iteration can be set by the user up to times but will be stopped if two consecutive filtered images do not show any further difference. Purpose: Uses a basic difference of Gaussian method for feature detection and a method which gives the difference between the original and a specified "Median" filter on a copy of the original image. How to: Difference of Gaussian needs the specification of 2 different Gaussian blurring radii and Difference from Median needs the specification of a Median kernel radius.

Purpose: This macro enables the color coding of the time or volume dimension in stacks and hyperstacks. Multi-channels need to be split up before color coding can be done. The macro is based on the idea of the plugin from Kota Miura and Johannes Schindelin. In contrast to the latter, it keeps the coded stack besides the creation of an additional color-coded z-projection.

How to: Given that you start with a hyperstack, you can choose between time and volume to be color coded. You can choose to create a z-projection by choosing from different projection types as available in the ImageJ "Z-Project" function. Furthermore, a separated calibration bar can be created which will be horizontal for coded time stacks and vertical for coded volume stacks.

Purpose: This macro enables the color coding of particles in an 8-bit binary image according to the number of neighbors of each individual particle. Depending on the method chosen, different neighbor particles will be considered during the analysis.

Particle Detection

How to: Specify the analysis parameters same input as for "Analyze Particles According to the calibration bar you can interpret the color coding. Methods: "Voronoi" analyzes the paticles according to the directly correlated voronoi map. This might underestimate the real number of neighbors and is rather suitable for roundish structures. Purpose: This macro statistically determines if particles according to their ultimate eroded point, UEP in a 2D image are likely to be randomly distributed, self-avoiding or build clusters.

How to: Starting with a binary image the setup dialog allows similar as the "Analyze Particles" to exclude particles by size and circularity as well as edge touching particles and include holes. The evaluation can be based on the mean nearest neighbor distance or the median nearest neighbor distance. The latter is suitable to better minimize the influence of outliers.

Method: The UEPs of the particles are generated and the nearest neighbor distance is determined for each particle. According to particle number and analyzed area the theoretical nearest neighbor distance is calculated using the formula: 0. This assumption ignores differences in particle size, so far. It is assumed that in the case of normally distributed particles, the mean equals the median. Thus, the method is implemented for the comparison of both, mean and median, from the assumption with the measured values.

The measured mean or median nearest neighbour distance is statistically compared to the theoretical one. Therefore, first a suitable test method is determined according to the homogeneity of the variance using an F-Test. This finally decides about the use of either a Student's T-Test or a Welch Test for the final statistical evaluation. This might be changed in future.

Show next edition. Covers the basics of particle detection mechanisms, as well as the techniques and detectors from the smallest to the largest that are on the market today Provides information on all application fields including nuclear and particle technology, geological dating, space science, medical and life sciences, and radiation protection Richly illustrated and includes imaging and imaging systems. Buy Print.

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About this book The handbook centers on detection techniques in the field of particle physics, medical imaging and related subjects. Show all. Read this book on SpringerLink. Detailed tables and diagrams will make this a very useful handbook for the application of these techniques in many different fields like physics, medicine, biology and other areas of natural science.

Spokesperson of a cosmic ray experiment muon spectra, charge ratio and interactions of muons at DESY, Hamburg - Visiting professor at the University of Tokyo and , doing cosmic ray work with Prof.

Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging
Handbook of Particle Detection and Imaging Handbook of Particle Detection and Imaging

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