Operators/Tools/Interactive/LabelImageEditor: Difference between revisions

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===== Main features =====  
===== Main features =====  
* subsequently displays images provided in an input folder to the user
* images provided in an input folder are subsequently displayed to the user
* regions, i.e. connected components, can be removed by simple mouse-clicks
* regions, i.e. connected components, can be removed by simple mouse-clicks


===== Usage =====
===== Usage =====
The editor is dedicated to the analysis of label images. In a label image each region or connected component, respectively, is assumed to be marked with a unique intensity or color value, i.e. all of its pixels should share this value. The editor allows to remove regions from such images by just clicking somewhere into a region to be removed. The region will then be removed by setting all its pixels to a value of zero.<br>
The editor takes as input a directory from where the images are loaded one after the other. Once an image is displayed to the user edit operations can be performed. Subsequently by clicking the "Next button" the current image can be saved to disk and the next image is loaded.<b>
'''Important notice''': There is currently no ''Undo'' function. If you accidentally deleted too many regions, either run the editor once again on the complete directory or copy the corresponding image to a separate folder and run the editor on that folder. Results of a former run will be overwritten without further inquiry.
To run the LabelImageEditor perform the following steps:
To run the LabelImageEditor perform the following steps:
* install MiToBo by following the instructions on the [[Installation]] page
* install MiToBo by following the instructions on the [[Installation]] page
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This will bring up the operator window of the LabelImageEditor.
This will bring up the operator window of the LabelImageEditor.


* Input data:<br>The operator analyzes all images in the given input folder, expecting images to have the extension ".tif".<br> If the images contain more than one channel, the first channel is used. In addition, for each image<br> 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.<br> These files should have the same basenames like the corresponding images, but either end on "-mask.tif" in case of label images,<br> 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.<br><br> If label images are used as segmentation masks, the labels of individual cells in the label images are used as unique identifiers for the cells, i.e., are used as labels in the various plots. If the segmentation data is given in terms of ROIs a unique identifier is derived from the order of the cell boundaries in the ROI set. Note that in the latter case a mask image is written to the output directory as additional output where the cells are marked by their identifiers to allow for easier assessment of the results.<br><br>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.<br> 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: <code>groupName_imageID.tif</code><br>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.<br>If the image names do not follow these requirements, all images are treated as a single group.<br><br>
* Input data:<br>The operator reads the contents of a given input image directory and displays all images one after the other to the user. The user can edit the images, i.e. remove labeled regions, by clicking with the left mouse button somewhere into the region to remove.<br> Clicking the "Next" button at the bottom will save the current image to disk and proceed with the next image in the folder. <br><br>
* Output data:<br>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:
* Output data:<br>For each input image the operator will save a corresponding output image, either in the same folder or a specific output folder if provided. Output images will share the name of the corresponding input image extended with substring "-edited".  
** ''*-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<br><br>
* Parameters:
* Parameters:
{|class="wikitable"
{|class="wikitable"
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|Description
|Description
|-
|-
|''Image directory''
|''Input Directory''
|directory where the input image data can be found
|directory from where the images to process will be loaded
|-
|''Mask directory''
|directory where the label images or region boundary files can be found
|-
|''Mask format''
|format of the segmentation data files: LABEL_IMAGE = images with unique labels for each cell and a value of zero for the background / IJ_ROIS = set of ImageJ 1.x ROIs, one ROI for each cell
|-
|''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;<br> this option is helpful if the features have already been calculated ones and only the parameters of the clustering should be changed
|-
|''Feature directory''
|directory where the features should be saved or - in case they are already available - from where they are read; the directory can be the same as the output and working directory
|-
|''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
|-
|''Isotropic calculations''
|the texture features are derived from co-occurence matrixes; if this flag is enabled features for different directions are averaged,<br> otherwise all individual directions are preserved (resulting in larger, but also more informative feature vectors)
|-
|-
|''Number of feature clusters''
|''File Filter''
|number of clusters in first stage, i.e., number of expected structural patterns in the images
|String which needs to be contained in the names of image files to be processed, file names not containing the pattern will be skipped. For example, if the string ".tif" is provided, only images ending with ".tif", i.e. in TIFF format, will be considered, the string "cell" would select only images containing the word "cell" somewhere in their name. Note that as string any valid Java regular expression is admissible. See for example [http://www.vogella.com/tutorials/JavaRegularExpressions/article.html this website] for more details on regular expressions.
|-
|-
|''Do PCA in stage II?''
|''Output directory''
|allows to enable/disable PCA on the cluster distribution vectors prior to the pairwise distance calculations; by default enabled
|optionally an output directory can be specified where the edited result label images are saved; if separate output folder is provided the result images will be stored in the input folder
|}
|}
===== Additional Tools =====
The hierarchical clustering in stage II of our approach as described in the paper has been done using the [http://deim.urv.cat/~sgomez/multidendrograms.php MultiDendrograms] software.<br> In principal every hierarchical clustering tool can be applied. <br>
The basis for the hierarchical cluster analysis is the file ''AllImagesPairwiseDistanceData.txt'' to be found in the output directory upon termination of an analysis run. It contains a matrix of pairwise Euclidean distances of the (optionally dimension-reduced) cluster distribution vectors of all cells. The file can directly be loaded by MultiDendrograms, for other tools format convertion might be necessary.<br>
You can download the latest version of MultiDendrograms from its webpage: [http://deim.urv.cat/~sgomez/multidendrograms.php]
===== Sample data =====
For testing the ''ActinAnalyzer2D'' operator we provide some sample data:
* [http://www.informatik.uni-halle.de/mitobo/downloads/actin_examples_update.zip ActinAnalyzer2D sample data for MiToBo >= 1.8]
* [http://www.informatik.uni-halle.de/mitobo/downloads/actin_examples.zip ActinAnalyzer2D sample data for MiToBo <= 1.7]
(With release 1.8 the handling of file names and automatic deduction of mask file names changed, thus, image names in the sample data archive had to be updated.)
The archive contains the following sub-folders:
* ''imageData'': test images that were used in the ICPR publication mentioned above
* ''maskData'': corresponding label images
* ''featureData'': pre-calculated features for the cells (re-calculating the features may require up to an hour, depending on the machine used)
* ''resultData'': sample results calculated on the given data
In addition, in the archive a file with a sample parameter configuration for the operator can be found. The parameters are those used for producing the sample results. Once the operator has been started the file can be loaded via the '' 'File' '' menu and its entry '' 'Load Settings' ''. Note that you need to set the image and mask directories, and also the feature directory according to your local file system structure and the place to where you extracted the zip file.
For more information on the data and the morphological analysis of the cells, see<br> <br>
''Anne Zirkel, Marcell Lederer, Nadine Stöhr, Nikolaos Pazaitis, and Stefan Hüttelmaier''<br>
'''''IGF2BP1 promotes mesenchymal cell properties and migration of tumor-derived cells by enhancing the expression of LEF1 and SNAI2 (SLUG)'''''<br>
''Nucleic Acids Res. Jul 2013; 41(13): 6618–6636. Published online May 15, 2013. doi: 10.1093/nar/gkt410'', [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3711427/ Article]


=== Updates ===
=== Updates ===
----<br>
----<br>


'''December 2016'''
'''March 2018'''
* In MiToBo 1.8.4 and MiToBo-plugins 1.8.3, respectively, which have recently been released, some bugs in the Actin Analyzer plugin were fixed, see latest news at the top of this page.
* The LabelImageEditor has been released in MiToBo 1.8.8 and MiToBo-plugins 1.8.8, respectively.
 
'''October 2016'''
* Sample data archive has been updated due to some changes in file name handling.
 
'''July 2014'''
* Released first version of Actin Analyzer as published in ICPR 2014.

Revision as of 10:43, 13 July 2018



Label Image Editor

MTBOperatorControlFrame-LabelImageEditor.png

The Label Image Editor is available since release version 1.8.8 of MiToBo.

Latest News

The Label Image Editor has been released for user-friendly editing of label images.

Name of Plugin/Operator

de.unihalle.informatik.MiToBo.tools.interactive.LabelImageEditor
(available since MiToBo version 1.8.8)

Main features
  • images provided in an input folder are subsequently displayed to the user
  • regions, i.e. connected components, can be removed by simple mouse-clicks
Usage

The editor is dedicated to the analysis of label images. In a label image each region or connected component, respectively, is assumed to be marked with a unique intensity or color value, i.e. all of its pixels should share this value. The editor allows to remove regions from such images by just clicking somewhere into a region to be removed. The region will then be removed by setting all its pixels to a value of zero.
The editor takes as input a directory from where the images are loaded one after the other. Once an image is displayed to the user edit operations can be performed. Subsequently by clicking the "Next button" the current image can be saved to disk and the next image is loaded. Important notice: There is currently no Undo function. If you accidentally deleted too many regions, either run the editor once again on the complete directory or copy the corresponding image to a separate folder and run the editor on that folder. Results of a former run will be overwritten without further inquiry.

To run the LabelImageEditor perform the following steps:

  • install MiToBo by following the instructions on the Installation page
  • run MiToBo and start the operator runner by selecting the menu item MiToBo Runner from Plugins -> MiToBo
  • in the selection menu navigate to 'de.unihalle.informatik.MiToBo.tool.interactive' and select the operator LabelImageEditor

This will bring up the operator window of the LabelImageEditor.

  • Input data:
    The operator reads the contents of a given input image directory and displays all images one after the other to the user. The user can edit the images, i.e. remove labeled regions, by clicking with the left mouse button somewhere into the region to remove.
    Clicking the "Next" button at the bottom will save the current image to disk and proceed with the next image in the folder.

  • Output data:
    For each input image the operator will save a corresponding output image, either in the same folder or a specific output folder if provided. Output images will share the name of the corresponding input image extended with substring "-edited".
  • Parameters:
Name Description
Input Directory directory from where the images to process will be loaded
File Filter String which needs to be contained in the names of image files to be processed, file names not containing the pattern will be skipped. For example, if the string ".tif" is provided, only images ending with ".tif", i.e. in TIFF format, will be considered, the string "cell" would select only images containing the word "cell" somewhere in their name. Note that as string any valid Java regular expression is admissible. See for example this website for more details on regular expressions.
Output directory optionally an output directory can be specified where the edited result label images are saved; if separate output folder is provided the result images will be stored in the input folder

Updates


March 2018

  • The LabelImageEditor has been released in MiToBo 1.8.8 and MiToBo-plugins 1.8.8, respectively.
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