H-tortuosity-by-iterative-erosions (3D)

H-tortuosity-by-iterative-erosions for the characterization of 3D volumes: Computation of the H-tortuosity estimator of the microstructure, as seen by a spherical particle of given size. In addition to keeping the properties of the H-tortuosity, this descriptor is linked to the notion of constrictivity, characterizing...

H-tortuosity-by-iterative-erosions (2D)

H-tortuosity-by-iterative-erosions for the characterization of 2D images: Computation of the H-tortuosity estimator of the microstructure, as seen by a spherical particle of given size. In addition to keeping the properties of the H-tortuosity, this descriptor is linked to the notion of constrictivity, characterizing...

H-tortuosity (3D)

Computation of the H-tortuosity estimator for the characterization of 3D volumes: a scalable topological descriptor providing a 3D map of mean tortuosities and final scalar values, the H-scalars, assessing the average variations of the morphological tortuosity with the scale.. This descriptor is based on a Monte Carlo...

H-tortuosity (2D)

Computation of the H-tortuosity estimator for the characterization of 2D images: a scalable topological descriptor providing a 2D map of mean tortuosities and final scalar values, the H-scalars, assessing the average variations of the morphological tortuosity with the scale. This descriptor is based on a Monte Carlo...

M-tortuosity-by-iterative-erosions (2D)

M-tortuosity-by-iterative-erosions for the characterization of 2D images: Computation of the M-tortuosity estimator of the microstructure, as seen by a spherical particle of given size. In addition to keeping the properties of the M-tortuosity, this descriptor is linked to the notion of constrictivity, characterizing...

M-tortuosity (2D)

Computation of the M-tortuosity estimator for the characterization of 2D images: a scalable topological descriptor providing a 2D map of mean tortuosities, a set of M-coefficients, of which the histogram is meaningful, and a final scalar value, the M-scalar, assessing the geometric tortuosity of the overall...

Deterministic M-tortuosity (3D)

Deterministic version of the M-tortuosity with imposed starting points set, for the characterization of 3D volumes. Real images, from electron tomography for instance, with a meaningless void can be described now. More details can be found using the links below. 23/09/20: bugs correction 29/09/20: update

Deterministic M-tortuosity (2D)

Deterministic version of the M-tortuosity with imposed starting points set, for the characterization of 2D images. Real images, from electron tomography for instance, with a meaningless void can be described now. More details can be found using the links below. 23/09/20: bugs correction 29/09/20: update

Edge-preserving filter

Noise reduction of a grayscale image preserving the sharp edges of objects. Is based initially on the work of Kuwahara, then Schulze and Pearce. In contrast to the latter, which retain for a given point, the local mean of the lowest variance, a linear combination of the local means in the vicinity is held according to...

3D surface area

3D surface area measurement of object(s) inside a volume. Each connected components can be consider as one object, or the entire volume as only one object. The calculation is from [Lindblad, 2005] : the estimation is performed with a “marching cube” approach using local weights. Two kind of local weights are...

Negative

Compute the negative of an 8-bit grayscale image or volume.

Extract ROI from binary mask

Extract the region of interest (ROI) of an image using a binary image (supported formats are .bmp, .png, .jpg, .tiff, and .fda). The region of interest is extracted within a new image of the same size as the original image. Outside the region of interest, the intensity values are zero. Zero intensity values from the...

SAXS intensity from projection

Small Angle X-ray Scattering intensity computed from projections of a 3D volume. Projections should be of size of power of two. The computed diagram is proportional to the square of the Fourier transform modulus of the projection of the object along one axis. The calculation is prone to strong finite size effects....

Projection

Orthogonal projection of a volume along x-axis, y-axis, or z-axis. This plugin can compute also opaque projection.

Extraction of porosity

Extraction of porosity from binary image taking into account external roughness. This plugin operates with a morphological closing by a disc whose radius can be automatically estimated, followed by a geodesic morphological opening. This method is defined and used in [Moreaud et al. 2008].

Extract patches from image

Extract patches from image using graphical interface. Update: 200200923: adding normalization for 8 bits or 24bits RGB image.

Rugosity analysis

Rugosity / texture analysis by H-maxima and area of influences on zones of interest drawn over the image. Computation of local maxima or minima, then area of influence, and for each area of influence, extraction of the surface area, minimum, and maximum intensity values. The zones of interest are directly defined by...

Homogeneization Dielectric Permittivity FFT scheme

An efficient method to solve the problem of homogenization of physical properties of heterogeneous media, such as dielectric permittivity, is the implementation of numerical solutions, before estimating the effective properties by spatial average of the solution. The input data is a 3D binary volume or a 2D binary...

Get plane

Extract cut plane(s) of a volume. Update 20200423 : extraction of all cut planes along one axis.

Histogram

Compute the histogram of an image.

ARFBF Morphological Analysis

In the case of active phases observable in the form of stacked sheets, the classic strategy is to segment these sheets individually and characterize their length or curvature [Celse, 2008], which may related to actibity [Gandubert, 2006]. Since the images are quite noisy at this level of resolution, this type of...

Mathematical morphology binary geodesic operations 2D and 3D

Mathematical morphology has been introduced by Matheron and Serra [Serra, 1982] in the late 1960's. It defines among others, two basic operators: dilation and erosion. These tools and their combinations are powerful and widespread for filtering and image analysis. For instance, operations such as opening, closing are...

Workshop GdR NanOperando 2019

A workshop dedicating to the potential of plug im! platform to extract quantitative information from data from in situ experiments, therefore "dynamic" by nature! Includes a package with a set of plug im! modules, working data files, and pdf slides to guide users.

Luminosity drift correction by morphological approach

Luminosity drift correction by TopHat operation combined with smoothing filter and / or LIP substraction.

Criterion opening

Opening by criterion, usually referred to as attribute opening (Breen & Jones, 1996; Serra, 1988; Walter, 2003; Salembier et al., 1998) is a class of more general connected filters (Salembier&Wilkinson, 2009). In binary context, this operation is a filter on the set of the connected components (CC) based on criterion...

Image sequence analysis of binary objects

Calculates the morphological analysis and movement of binary objects in an image sequence. Each object is individually tracked over the entire image sequence. The input data must be a 3D volume, each plane on the Z axis corresponding to an image of the image sequence. Morphological characterization : surface area, ...

SAXS for Boolean models of spheres

This plugin computes the scattering intensity from the analytical covariance of multiscale models built from union and intersection of Boolean models of spheres with Gamma or lognormal radius distributions. Another numerical approach, using projections of realisations for any kind of models, can be find in Sorbier et...

CPE Lyon Morphological Random Models : introduction, course, 2019

CPE Lyon Morphological Random Models : introduction, course, 2019

Extraction of connected components in individual files

Extraction and saving of connected components in individual images, in a directory of your convenience. Update 20190909 : fixed a bug related to saving individual images. Update 20191004 : fixed a bug related to saving individual images.

Closing ends

Considering edges of connected components as ends, each end pixel is linked to another end pixels if the two pixels belong to different connected components, and if the distance between the two pixels is less than a threshold. This operation can be useful in the case of disconnected filamentous objects and it is used...