Granulometric Filtering

Granulometric Filtering

Author: Dimiter Prodanov (D.Prodanov at lumc.nl)

History: 2005/10/21: First version

Source: Contained in Gran_Filter.jar, which can be opened using a ZIP utility

Installation: Download Gran_Filter.jar to the plugins folder, or subfolder, restart ImageJ, then run the plugin using the Plugins/Morphology/Gran filter command.

Description: This plugin performs granulometric filtering of digital images. The algorithm is described in Prodanov et al. J. Neurosci. Methods 2005 doi:10.1016/j.jneumeth.2005.07.011 More information is at www.diagnosticarea.com.
Perhaps the oldest and most frequently used technique in the empirical sciences to quantify the size of solid particles is to use a series of sieves with increasing mesh openings. To quantify the properties of discrete sets of objects Matheron theorized empirical sieving into the formal concept of mathematical granulometry (Matheron, 1975). Granulometry was later applied in image analysis to both binary and continuous tone images (Serra, 1982). In a way similar to sieving grains, pixels comprising an image are "sieved" according to their connectivity to similar pixels imposed by a certain primitive geometric body termed Structuring Element (SE). An integral characteristic of granulometry is the distribution of pixels with respect to the diameters of the used SE-s. A local maximum in its normalized first derivative, the granulometric size density (G(d)), indicates the presence of a number of objects matching the particular SE. Moreover, granulometry can act as a band-pass filter capable of discriminating grains of a certain size based on their similarity to a SE. By exploiting this property, images can be simplified substantially to successfully isolate various classes of objects.
About the user interface:

Type of SE: can be one of circle, diamond, square, hor. line or vert. line
Radius of SE: the radius or half/length [square, lines] of SE
Step: the increse from the Radius - Radius2=Radius+step
Euclicean distance: check to compute the Euclidean distance/area

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Author:	Dimiter Prodanov (D.Prodanov at lumc.nl)
History:	2005/10/21: First version
Source:	Contained in Gran_Filter.jar, which can be opened using a ZIP utility
Installation:	Download Gran_Filter.jar to the plugins folder, or subfolder, restart ImageJ, then run the plugin using the Plugins/Morphology/Gran filter command.
Description:	This plugin performs granulometric filtering of digital images. The algorithm is described in Prodanov et al. J. Neurosci. Methods 2005 doi:10.1016/j.jneumeth.2005.07.011 More information is at www.diagnosticarea.com. Perhaps the oldest and most frequently used technique in the empirical sciences to quantify the size of solid particles is to use a series of sieves with increasing mesh openings. To quantify the properties of discrete sets of objects Matheron theorized empirical sieving into the formal concept of mathematical granulometry (Matheron, 1975). Granulometry was later applied in image analysis to both binary and continuous tone images (Serra, 1982). In a way similar to sieving grains, pixels comprising an image are "sieved" according to their connectivity to similar pixels imposed by a certain primitive geometric body termed Structuring Element (SE). An integral characteristic of granulometry is the distribution of pixels with respect to the diameters of the used SE-s. A local maximum in its normalized first derivative, the granulometric size density (G(d)), indicates the presence of a number of objects matching the particular SE. Moreover, granulometry can act as a band-pass filter capable of discriminating grains of a certain size based on their similarity to a SE. By exploiting this property, images can be simplified substantially to successfully isolate various classes of objects. About the user interface: Type of SE: can be one of circle, diamond, square, hor. line or vert. line Radius of SE: the radius or half/length [square, lines] of SE Step: the increse from the Radius - Radius2=Radius+step Euclicean distance: check to compute the Euclidean distance/area