Applications/ActinAnalyzer2D

Actin Analyzer 2D

The Actin Analyzer 2D is available since release version 1.4 of MiToBo.

Related Publications

• B. Möller, E. Piltz and N. Bley, "Quantification of Actin Structures using Unsupervised Pattern Analysis Techniques".
  In Proc. of Int. Conf. on Pattern Recognition (ICPR '14), IEEE, Stockholm, Sweden, August 2014, accepted for publication.

Name of Plugin/Operator

de.unihalle.informatik.MiToBo.apps.actinAnalysis.ActinAnalyzer2D
(available since MiToBo version 1.4)

Main features

• automatic extraction of different structural patterns by unsupervised texture analysis and clustering
• co-occurence matrices and Haralick features are currently used for texture charakterization
• structure quantification performed based on cell-wise cluster distributions

Usage

• Input data:
  The operator analyzes all images in the given input folder, expecting images to have the extension ".tif".
  If the images contain more than one channel, the first channel is used. In addition, for each image
  either a label image of pre-segmented cell regions or a set of region boundaries is expected to be available in the given mask folder.
  These files should have the same basenames like the corresponding images, but either end on "-mask.tif" in case of label images,
  or on ".zip" or ".roi", respectively, in case of region sets. Note that the operator currently only accepts ImageJ 1.x ROI sets as input.
  
  The operator is able to automatically consider different groups of cells in its analysis, e.g., generate plots of the cluster distributions for each group individually.
  However, the group membership of each image has to be encoded in its filename. In detail, the operator expects the file names to obey the following structure: groupName_imageID.tif
  In particular, there must not occur more than a single underscore in each filename and the imageID must be unique for each image of a group.
  If the image names do not follow these requirements, all images are treated as a single group.
  
  
• Output data:
  The operator displays the cluster distributions for each group of cells as stacked bar plots and box-whisker plots. In addition, it writes several files to the given output folder:
  • *-features.txt: feature data for each image
  • *-features.tif: image stack visualizing the feature data
  • *-features-config.ald: configuration of the operator in this run
  • *-clusterDistro.txt: cluster distributions per image
  • *-clusters.tif: pseudo-colored image illustrating the cluster distribution per image
  • AllImagesClusterStatistics.txt: cluster distribution raw data for all images
  • AllImagesSubspaceFeatures.txt: if PCA is applied to the cluster distributions prior to the distance calculations, this file contains the subspace feature vectors
  • AllImagesPairwiseDistanceData.txt: matrix of pairwise Euclidean distances for distribution vectors, can be examined, e.g., with Multidendrograms (see below)
  • *-distribution.png: for each cell group a stacked bar plot is saved showing the cluster distribution for each cell of the group
    
    
• Parameters:

Name	Description
Image directory	directory where the input image data can be found
Mask directory	directory where the label images or region boundary files can be found
Output and working directory	directory to which the result files and intermediate data is written
Calculate features	if disabled the operator expects the features to be already present in the input directory and skips the (time-consuming) feature calculations; this option is helpful if the features have already been calculated ones and only the parameters of the clustering should be changed
Isotropic calculations	the texture features are derived from co-occurence matrixes; if this flag is enabled features for different directions are averaged, otherwise all individual directions are preserved (resulting in larger, but also more informative feature vectors)
Tile size x/y	size of the sliding window used for feature calculations, should be chosen according to the resolution of the input images
Tile shift x/y	shift of the sliding window, if the shift is smaller than the tile size sliding windows overlap
Distance	pixel-pair distance in co-occurence matrix calculations
Set of directions	directions to be considered in co-occurence matrix calculations
Number of feature clusters	number of clusters in first stage, i.e., number of expected structural patterns in the images

Updates

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July 2014

• Released first version of Actin Analyzer as published in ICPR 2014.