The programs in this section are dedicated to basic 2D image processing tasks, like filtering by invoking plug-ins (Plugin type: 2dimage/filter), combining pairs of images (Plugin type: 2dimage/combiner), creating test images (Plugin type: 2dimage/creator), and evaluating some statistics.
Evaluate average intensities of an image seriesThis program is used to evaluate the average intensity and its variation of a series of images in a given masked region.
This program is used to combine two binary images by some kind of operation.
This program evaluate the average or maximum distance of a mask given by a binary image to an image representing a distance map and prints the result to stdout. The distance map can be obtained by running the filter 'diatance' on a binary image.
This program runs a combined fuzzy c-means clustering and B-field correction to facilitate a fuzzy segmentation of 2D image. cf D.L. Pham and J.L.Prince, "An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities", Pat. Rec. Let., 20:57-68,1999
mia-2dgrayimage-combine-to-rgb
This program combines up to three gray scale image to a three channel RGB image with eight bit per color channel. The input images must be gray scale of eight bit colordepth. If at input one channel is not given, it will be set to zero. at least one input channel must be given.
This program evaluate the dice index of two binary masks given as binary images. The result is written to stdout.
Combine two image by a given operation.
This program is used to create test images.
This program runs a series filters on a given input image. The filters are given as extra parameters on the command line and Ware run in the order in which they are given. To obtain a list of available filters you may run 'mia-plugin-help filter/2dimage' from the command line
This program runs a series filters on a series of consecutive numbered input image. The filters are given as extra parameters on the command line and are run in the order in which they are given.
This progranm is used to evaluate some statistics of an image. Output is Mean, Variation, Median, Min and Max of the intensity values.
mia-2dimageseries-maximum-intensity-projection
This program is used to evaluate the per-pixel maximum intensity of an image series.
This progranm is used to evaluate some statistics of an image. Output is Mean, Variation, Median, and Median Average Distance of the intensity values. The program allows one to set a lower threshold and to cut off a percentage of the high intensity pixels
Merge two images by pixel-wise linearly combining their intensities.
This program evaluates the pixel-wise accumulated intensity variation of a set of image given on the command line. If the input image files contain more then one image all images are used. All images must be of the same size.
This program implements a variation of the paper:Mohamed N. Ahmed et. al, "A Modified Fuzzy C-Means Algorithm for Bias Field estimation and Segmentation of MRI Data", IEEE Trans. on Medical Imaging, Vol. 21, No. 3, March 2002, changes are: p=2, and exp
This program is a implementation of a fuzzy c-means segmentation algorithm
This program runs the segmentation of a 2D image by applying a localized c-means approach that helps to overcome intensity inhomogeneities in the image. The approach evaluates a global c-means clustering, and then separates the image into overlapping regions where more c-means iterations are run only including the locally present classes, i.e. the classes that relatively contain more pixels than a given threshold.
This program runs the segmentation of a 2D image by applying a localized k-means approach that helps to overcome intensity inhomogeneities in the image. The approach evaluates a global k-means clustering, and then separates the image into overlapping regions where more k-means iterations are run only including the locally present classes, i.e. the classes that relatively contain more pixels than a given threshold.
mia-2dsegment-per-pixel-kmeans
This program runs the segmentation of a 2D image by applying a localized k-means approach that helps to overcome intensity inhomogeneities in the image. The approach evaluates a global k-means clustering, and then separates the image into overlapping regions where more k-means iterations are run only including the locally present classes, i.e. the classes that relatively contain more pixels than a given threshold.