Image segmentation

Image segmentation is (one of) the (few) concept(s) on the border between Image (pre)processing (Image->Image) and Image analysis (Image->Data).

Description

Image-processing algorithms developed at the MOSAIC Group for fluorescence microscopy. Tools included:

  • 2D/3D single-particle tracking tool which can be used to track bright spots in 2D/3D movies over time.
  • Optimal filament segmentation of 2D images. 
  • Curvature filters for image filtering, denoising, and restoration. 
  • Image naturalization for image enhancement based on gradient statistics of natural-scence images. 
  • Tool for automatically send and distribute jobs on clusters and get back the results.
  • Multi-region image segmentation of 2D and 3D images without needing to know the number of regions beforehand. 
  • Squassh for globally optimal segmentation of piecewise constant regions in 2D and 3D images and for object-based co-localization analysis. 
  • Tool for inferring spatial interactions between patterns of objects in images or between coordinates read from a file.
  • Tool for robust, histogram-based background subtraction well suited to correct for inhomogeneous illumination artifacts.
  • A tool to estimate the Point-Spread Function of the microscopy out of 2D fluorescence images.
  • A tool to measure the 3D Point-Spread Function of a confocal microscope from an image stack.
  • Addition of synthetic Poisson-distributed noise to an image in order to simulate shot noise of various signal-to-noise ratios. 
  • Convolution of an image with a Bessel function in order to simulate imaging with a microscope. 
  • A utility to detect bright spots in images and estimate their center. 
  • A utility to create manual segmentations to be used as ground truth to test and benchmark automatic segmentation algorithms.
  • A tool for replacing one color in an image with another color.
has topic
Description

Free-D is a three-dimensional (3D) reconstruction and modeling software. It allows to generate, process and analyze 3D point and surface models from stacks of 2D images. Free-D is an integrated software, offering in a single graphical user interface all the functionalities required for 3D modeling. It runs on Linux, Windows, and MacOS. Free-D is developed by the Modeling and Digital Imaging team of the Institut Jean-Pierre Bourgin, INRA Versailles, France.

Description

Free-D (http://free-d.versailles.inra.fr/) is a 3D reconstruction and modeling software. It is multiplatform, free (but not open source) tool for academic research and teaching.

Here is how to proceed, using Free-D:

1. Segmentation:

* load (a collection of) individual 3d stacks

* (optional for serial sections) perform a 2D registration to align image slices

* segment/reconstruct 3D contours using snakes

* segment 3D spots

2. Construct average cell:

* normalize the contours to compute a average cell, by registering/warping 3D contours/surfaces

3. Quantification:

* project each individual cell to the average one

* build density maps to analyze (cartography)

A few notes for current software version (till 10/2016):

* input file format: tiff (not able to import bioformats)

* currently results are saved in customized format, but there is an exportor to convert this format into fiji readable one

* import already generated contours is on the software's TODO list

need a thumbnail
Description

It is a tool to visualize and annotate volume image data of electron microscopy. Users can annotate objects (e.g. neurons) and skeleton structures. It provides the ability to overlaying the image data with user annotations, representing the spatial structure and the connectivity of labeled objects, and displaying a three dimensional model of it. It can be extended by plugins written in python. A similar, web-based implementation is being developed at webknossos.info. Example datasets are also available.

Annotation in Knossos
Description

MorphoLibJ is a library of plugin for ImageJ with functionalities for image processing such as filtering, reconstructing, segmenting, etc... Tools are based on Mathematical morphology with more rigorous mathematical approach than in the standard tools of ImageJ in particular for surface (or perimeter) measurements which are usually based on voxel counting.  

http://imagej.net/MorphoLibJ#Measurements

Among the features:

Morphological operations :  Dilation, Erosion, Opening,  Closing , Top hat (white and black), Morphological gradient (aka Beucher Gradient), Morphological Laplacian, Morphological reconstruction, Maxima/Minima , Extended Maxima/Minima -Watershed (classic or controlled) -Image overlay -Image labelling -Geodesic diameter -Region Adjacency Graph -Granulometry curves, morphological image analysis.

 

several steps of morphological segmentation of plant tissue using MorphoLibJ.