Image processing

SIMToolbox: a MATLAB toolbox for structured illumination microscopy SIMToolbox is an open-source, modular set of functions for MATLAB designed for processing data acquired by structured illumination microscopy. Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination. 2665

## About TANGO software is an open-source software for Analysis of Nuclear Genome Organization. It is composed of an ImageJ plugin for batch processing and analysis, and a R package for statistical analysis. Reference: 2528 ## Some key features - Image import uses bioimage formats. - Construction of workflow in GUI by choosing filters / segmentation strategy for - Prefiltering - Segmentation - Postfiltering - Isolated nuclei could individually be inspected, deleted from list and subjected for detailed analysis. - Uses MCIB3D library as backend. - Basic usage is to segment nucleus, crop them to single nucleus objects, segment substructures within objects and measure their properties. - Optionally R can be connected to do detailed analysis of results. - Uses MongoDB to manage huge data set.

Pandore is a standardized library of image processing operators. The current version contains image processing operators that operate on grayscale, color and multispectral, 1D, 2D and 3D images.

Link: Operator Index

The rapidSTORM project is an open source evaluation tool that provides fast and highly configurable data processing for single-molecule localization microscopy such as dSTORM. It provides both two-dimensional and three-dimensional, multi-color data analysis as well as a wide range of filtering and image generation capabilities. The general operation of rapidSTORM is described in Wolter et al (2012).

This protocol perform a median filter on the active sequence using the ImageJ rank filter plugin. Then, it converts the result back into Icy for display.

An example showing passing data between ICY and ImageJ using ImagePlus object. 

Image processing library for Python >The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a versatile set of image processing routines. It is written in the Python language. This SciKit is developed by the SciPy community. All contributions are most welcome!

Simple spatial filters can be used to suppress noise in raw image data (i.e. by averaging intensities). The best choice of filter depends on the nature of the noise, but Gaussian filtering works well for Poisson noise (i.e. commonly observed photon-counting shot noise); whereas a median filter is ideal for salt-and-pepper noise. A larger filter radius leads to stronger noise suppression but more blurring. The URL above describes the simple 2D spatial filters available in ImageJ, but similar filters are available in most software. For 3D data, 3D versions of these filters work best (since there are more pixels to average within the same radius).

This macro implements a filter that is meant to attenuate close to parallel intensity stripes in an image, such as often happening in light sheet microscopy. The results are usually decent even when the stripes show a large angular spread due to light sheet refraction at the sample surface. The filter can process a 3D stack but the processing is performed slice by slice.

Example image is available in the documentation link. 

A commercial image analysis software. It's interface allows to easily perform measurements and image analysis. Your actions can be recorded and a macro (in a basic script language) can then be created. Almost no knowledge in programming is needed. You can also use python. A SDK is also available to develop stand alone applications in c++. Additional modules allow to use specific operations (3D operators... Examples of available categories of operators : filtering, edge detection, mathematical morphology, segmentation, Frequency operations, mathematical/logical operations, measurements...

This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

==== Strengths

The major strengths are in speed and quality of documentation. Almost all of the functionality is implemented in for multiple dimensions. It can be used with other Python packages which provide additional functionality.

Mahotas and all packages on which it relies are open-source.

Drop Shape Analysis is a collection of two methods (DropSnake and LBADSA) for high-accuracy measure of contact angles for drop measurement.

A complete parametric framework and set of MATLAB tools for computing steerable wavelet frames in 2-D.

This CellProfiler module allows smoothen the image with a choice from various algorithms: - Fit Polynomial - Gaussian Filter - Median Filter - Bilateral Filter - Circular averaging - "Smooth to Average" filter.

GrayToColor takes grayscale images as input and assigns them to colors in a red, green, blue (RGB) image or a cyan, magenta, yellow, black (CMYK) image. Each color’s brightness can be adjusted independently by using relative weights.

Quantize a color image in any given number of colors.

Enhances the global contrast by equalizing the histogram. This plugin transforms pixel intensities so that they are uniformly distributed over the gray-scale range. It operates on the selected channel of each image of a sequence. This operation is also called "histogram flattening".

This plugin parses arbitrary mathematical expressions and compute results using images as variables.

Compute and display the histogram of a sequence, with a more accurate control on the histogram parameters (such as the number of bins) than the built-in Icy widget. In particular, the histogram can be computed either over a whole sequence or over a sub-region defined by a ROI.

ImageMath performs simple mathematical operations on image intensities, like addition, subtraction, multiplication, division...

A fork of PIL python package, with small collection of image import/export and image processing modules. See [Reference Documentation](http://pillow.readthedocs.org/en/latest/reference/index.html) for more details. Though this package mostly works in any platform, some of them are limited to Windows. This package is a part of [pythonxy](https://code.google.com/p/pythonxy/).

This plugin calculates the Nyquist sampling, in the radial and axial direction for your Microscope.

The fractional splines are an extension of the polynomial splines for all fractional degrees α > -1. Their basic constituents are piecewise power functions of degree α. One constructs the corresponding B-splines through a localization process similar to the classical one, replacing finite differences by fractional differences. The fractional B-splines share virtually all the properties of the classical B-splines, including the two-scale relation, and can therefore be used to define new wavelet bases with a continuously-varying order parameter

The smoothing is applied by the way of a bi-exponential filter, itself realized by a pair of one-tap recursions. It is therefore very fast; moreover, its computational cost is truly independent of the amount of smoothing. Meanwhile, the preservation of edges is obtained by a range filter akin to the range filter found in a bilateral filter. More technical details are available here.

The plugin allows one to control the amount of smoothing, the type of range filter, its broadness, and to iterate the filter several times if desired. We illustrate in Figure 2 a possible outcome of this filter. Here, we iterated the BEEPS 10 times with a Gaussian range filter, σ = 10, and the spatial decay λ = 0.1.

This plugin perform various 3D filters on 8-bits or 16-bits gray-levels stacks :

• 3D median

• 3D mean

• 3D minimum

• 3D maximum

• 3D maximum local

• 3D tophat (detect bright spots, TH=I-max(min(I)) )

Short Description

Execute some simple math operations on sequences, such as addition, product, absolute value extraction, rounding to the closest integer, etc. All the operations are executed pointwise.

Produces files that allow individual batches of images to be processed separately on a cluster of computers.

Process an image using Perreault’s modern constant-time median filtering algorithm.

This C routine is based on the following two papers:

• M. Unser, A. Aldroubi and M. Eden, "B-Spline Signal Processing: Part I--Theory," IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 821-832, February 1993.
• M. Unser, A. Aldroubi and M. Eden, "B-Spline Signal Processing: Part II--Efficient Design and Applications," IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 834-848, February 1993.

It implements the resampling of an image/volume under an affine transformation. The continuous model is based on splines of degree 0 (nearest neighbours), degree 1 (linear interpolation), degree 2 (quadratic), 3 (cubic), 4 (quartic), 5 (quintic), 6 and 7. By convention, the affine transformation is given by an homogenous matrix; the operation performed is output(A x) = input(x). In other words, a matrix given by

A = { {2,0,0,0}, {0,2,0,0}, {0,0,2,0}, {0,0,0,1} }

will result in a magnification by a factor 2 in linear dimensions. In case the desired operation would be output(x) = input(A x), it should be easy to modify the code (mainly: remove the call to invertTrsf() and assign invTRsf = trsf). The origin relative to which the transformation is performed is given with respect to the center of the top-upper-left voxel; the coordinate system is right-handed. Output values in need of extrapolation are set to the value background.

The input volume (the volume to transform) is given by inPtr, a pointer to an array of float values in raster order. More precisely, the values are ordered such that the x values are incremented first, then the y values, finally the z values. The size of the volume is given by nx, ny and nz, respectively. The output volume has necessarily the same size and follows the same organization. Its memory space cannot be shared with the input, and is supposed to be already allocated when the affineTransform() routine is called.

All routines are local, with the exception of the routine to call, named affineTransform(), and the routine errorReport(). The latter is not included in this distribution; its purpose is to display an error message given by a C-string. Else, the code is self-contained (provided a standard ANSI-C environment is available). It consists of only two files: affine.h and affine.c.

A Mathematica package available for the symbolic computation of exponential spline related quantities: B-splines, Gram sequence, Green function, and localization filter.

Applies average filtering to images in n-dimensions

Some functions for PDE filtering.

The plugin performs stitching of images of a tiled scan to reconstruct the image of the whole sample.