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Medical Image Segmentation -Synthesis (MISS) Tool User Guide

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Contents

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Introduction. 1

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Version -(last update): 12/9/2024. 1

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The -Use of GUI. 1

- -

Main -options. 1

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Segmentation -Performance Metrics. 1

- -

Evaluation -of a single case. 1

- -

Load -data. 1

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To -view result 1

- -

Results -output 1

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Other -options. 1

- -

Performance -metrics computation on multiple cases. 1

- -

Load -data. 1

- -

Results -output 1

- -

Segmentation -synthesis. 1

- -

Load -data. 1

- -

Synthesis -Tools. 1

- -

Add -spiculations. 1

- -

Change -contour by Fourier Descriptor. 1

- -

Affine -transformation. 1

- -

Results -Output 1

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Batch -Processing. 1

- -

Parameters. 1

- -

Editing -operations for modification plan. 1

- -

Applying -modifications to mask(s). 1

- -

Save -& load modifications plan. 1

- -

The -Use of Command (Code) 1

- -

Main -functions. 1

- -

Function -for segmentation evaluation metrics: seg_measures. 1

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Functions -for segmentation synthesis. 1

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Add -spiculations: mod_spike. 1

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Change -contour by Fourier Descriptor: mod_fourier. 1

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Affine -transformation: mod_affine. 1

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An -example of segmentation evaluation. 1

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Implement -by GUI. 1

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Implement -by Command (Code). 1

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An -example of segmentation synthesis. 1

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Implement -by GUI. 1

- -

Implement -by Command (Code). 1

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- - -

 

- -

Introduction

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The Medical Image Segmentation -Synthesis (MISS) Tool is software written in MATLAB that allows a user to -produce synthetic segmentations and assess segmentation performance using a -wide range of performance metrics implemented within the code. The MISS -tool can be used through a graphical user interface (GUI) or as command-line -functions inserted into a user’s own code. The GUI allows for visualization of -the synthesis segmentation, interactive tuning of the synthesis parameters, and -display of the segmentation evaluation results. The command-line mode allows -for processing images in batches as well as providing flexible ways for users -to integrate the MISS tool with their applications.

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The MISS tool provides two basic -components:

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·         -Image segmentation synthesis function:  -This function allows a user to emulate a range of reader/algorithm -segmentations through the adjustment of an input (truth) segmentation mask with -various types of segmentation errors such as the addition of spiculations or -other types of systematic contour changes. The user inputs an initial -segmentation (truth) mask, and the image segmentation synthesis function -outputs a new segmentation contour that includes the selected segmentation -errors.

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·         -Segmentation performance evaluation function: -This function allows a user to compute 24 performance metrics, such as the Dice -coefficient, Jaccard index (IoU), Medical Similarity Index (MSI), and Hausdorff -Distance (HD), as part of a comprehensive segmentation evaluation applied to a -single or set of imaging studies. The user provides a truth mask and a -segmentation mask for each image, and the program outputs values for the -24-performance metrics chosen by the user.

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- -

Version -(last update): 12/9/2024

- -

The -Use of GUI

- -

The GUI -contains two options:

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·         -Segmentation -performance evaluation

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·         -Image -segmentation contour synthesis

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To -start the GUI, open the file “main_option.mlapp” and run it in MATLAB.

- -

Main -options

- -

When running -the MISS tool MATLAB-GUI, the Main Options window pops up.

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If you -have truth masks and binary segmentation results, to calculate segmentation performance -metrics, select 1: Computing Segmentation Metrics.

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If you -have truth masks, to generate synthetic segmentation contours based on the -truth masks, select 2: Synthesis of Segmentation Contours.

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When either -option is selected, the Main Options window is closed, and a new window will be -shown. There is a “Return to main window” button on the new window to go back -to the Main Options window.

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Segmentation Performance Metrics

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After clicking -the “Computing Segmentation Metrics” on the main options window, you will -see the window for Metrics Computation.

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“Return -to main window” button -on the new window is for going back to the main window.

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Evaluation -of a single case

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Load -data

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For evaluating -segmentation performance of a single case, you click the “Segmentation -contour” and “Reference contour” buttons in the “User Input -(Single)” panel to input one segmentation contour and one (reference) truth -mask.

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The -supported input file formats are: '*.jpg; *.tif; *.png; *.gif'. The image -should have only one object (connected component) as the segmentation or truth -mask (filled contour image).

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Note: -input images must have the same size.

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After -inputting an image, the file name of the image will show below the input button, -the metrics values and images will display automatically.

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To -view result

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The values -of 24 metrics show in the “Segmentation Metrics” frame. On the Segmentation -Viewer, the segmentation truth is shown as a blue mask, and the segmentation is -shown as a green contour.

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Note: -by default, on Metrics Computation window the calculations of Hausdorff -Distance (HD) and Average Hausdorff Distance (AVD) metrics are enabled. If you -want to exclude the two metrics, uncheck the “Compute Hausdorff Distances -(Slow)” box under the “Comparison” frame.

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More details of these metrics are -in the “Functions of segmentation evaluation metrics” section.

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Results -output

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The values -of 24 metrics auto-save in the “Workspace” of MATLAB, it is a MATLAB struct -value named “metrics_value”.

- -

- -

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By clicking -the “Save metrics values” button, these values will be saved in a CSV -file specified by the user.

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Other options

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·         -“Compute -Hausdorff Distances (slower)” check -box: on/off the calculations of Hausdorff Distance (HD) and Average Hausdorff Distance -(AVD) metrics.

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ßà -

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·         -“Display in -filled contour” check -box: on/off filling the area of segmentation. If checked the truth mask shows -in filled red area, and the segmentation shows in filled blue area with green -contour. Their overlapping area shows in purple.

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 ßà -

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Performance -metrics computation on multiple cases

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Load -data

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To load images and masks for multiple cases, you click -the “Segmentation contours” and “Reference -contours” buttons in the “User Input (Multiple cases)” panel to specify -segmentation masks and truth (reference)  masks.

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The -supported image formats are: '*.jpg; *.tif; *.png; *.gif'. The image should -have only one object (connected component) as the segmentation or truth mask.

- -

Note: -1) the numbers of segmentations and truth masks must be same and non-zero; 2) all -input images must have the same size; 3) the program assumes one-to-one -correspondence between the two lists, i.e., the first segmentation mask -corresponds to the first truth mask, and so on.

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After loading -the image files, the number of image files will show below the buttons, and -files’ names will print in MATLAB Command Window.

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Results -output

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After -loading the image files, you click the “Performance Metrics” button to -start computing the 24 metrics values for each pair of segmentations and truth masks, -and the results will be saved in a CSV file specified by the user.

- -

Segmentation -synthesis

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Load -data

- -

- -

First, you -click the “Load Truth Mask” button (the blue arrow on above picture) to -input a truth mask.

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The -supported image formats are: '*.jpg; *.tif; *.png; *.gif'. The image should -have only one object (connected component) as the truth mask.

- -

- -

After loading -a mask, the metrics values and images will display automatically, where the -metrics reflect the segmentation error of the synthetic contour against the -input truth contour. Without any modification, the synthetic segmentation is -identical to the truth mask.

- -

Note: -by default, on Synthesis of Contour window the calculations of Hausdorff -Distance (HD) and Average Hausdorff Distance (AVD) metrics are disabled due to -a slow processing. If you want to include the two metrics, check the “Compute -Hausdorff Distances (Slow)” box under the “Comparison” frame.

- -

The “Recover” -button is to undo all changes and recover the original mask image. For details -of the “Compute Hausdorff Distances (slower)” check box and “Display in filled -contour” check box, please see in the “Other options” section.

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Synthesis -Tools

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Our tool -provides three methods to modify the input truth mask and generate synthetic -segmentation contours:

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·         -Add -spiculations – “Spiculation” button. An example:

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 à

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·         -Contour change -by Fourier Descriptor – “Fourier” button. An example:

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 à

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·         -Affine -transformation – “Affine” button. An example:

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 à

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Each -method has several parameters for the user to tune in order to generate -different synthetic segmentations. For more details of these methods, please -see in the “Functions of segmentation synthesis” section.

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Add spiculations

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Parameters

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·         -Angle: angular location to put the -spiculation, it is an angle value ranging from 0 to 359 degree.

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·         -Height: the height of the -spiculation, in terms of the number of pixels.

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·         -Width: the width of the spiculation, in -terms of the number of pixels.

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For -technical details of these parameters, please see in the “2.3 -Spiculations” section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). -MISS-tool: medical image segmentation synthesis tool to emulate segmentation -errors. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. -12465, pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

Operations (note these apply to the other

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·         -Close: close the window.

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·         -Apply to -original: add the spiculation -to the input mask. This operation will reset the mask that has been modified by -other methods before adding a spiculation.

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·         -Apply to -current: add the -spiculation to the current mask. This operation is to add a spiculation to a -mask that has been modified by other methods (or to add multiple spiculations -to a mask).

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·         -Add params -to Batch Processing: -send and add all parameters of this modification to the Batch Processing window. -For more details, please see in the “Batch Processing” section.

- -

·         -Undo once: This operation will undo the last -modification and recover the previous mask. When multiple changes have been applied, -this operation can only undo the last change.

- -

Note: changes by all the three Synthesis methods are counted, -i.e., after the “undo once” in one Synthesis Tool window, the “undo once” -function in other Synthesis Tool windows will not work anymore before the next -modification is executed.

- -

After -clicking the “Apply to original” or “Apply to current”, the result will show on -the Segmentation Viewer. Here we show an example of:

- - - - - - -
-

Angle: 270 (degree)

-

Height: 20 (pixels)

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Width: 6 (pixels)

-

Apply to original.

- 		-

    à    -

-
- -

Change contour by Fourier -Descriptor

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- -

 

- -

Parameters

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·         -Detail: the number of lower-frequency -Fourier Descriptors to be kept (FDs beyond this will be set to zero); the larger -value for this parameter the more detail of the contour (high-frequency -components) will be kept; ranging from 1 to the “Total #”, which is the total -number of Fourier Descriptors for this contour.

- -

·         -Range: the number of Fourier -Descriptors to be modified; ranging from 0 to (Detail value – 1); the -larger value for this parameter the greater change is applied to the contour.

- -

·         -Magnitude: the magnitude of modification to -the Fourier Descriptors to be made according to the Range; the larger -value for this parameter the greater change is applied to the contour.

- -

For -technical details of these parameters, please see in the “2.4 Fourier -Descriptor” section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). -MISS-tool: medical image segmentation synthesis tool to emulate segmentation -errors. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. -12465, pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

After -clicking the “Apply to original” or “Apply to current”, the result will show on -the Segmentation Viewer. Here is an example:

- - - - - - -
-

Detail: 20

-

Range: 12

-

Magnitude: 6

-

Apply to original.

- 		-

    à    -

-
- -

Affine transformation

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- -

Parameters

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·         -Resize: the ratio of size change on -x-axis (vertical) and y-axis (horizontal). Here we show an example for x = 1.5, -y=2 applying to original mask, without other changes:

- -

 à

- -

·         -Rotation: the degree of counter-clockwise -rotation around the center. Here is an example for rotation of90 degrees:

- -

 à

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·         -translation: in terms of the number of pixels. -Here is an example:

- -

 à

- -

For -technical details of these parameters, please see in the “2.1 Affine -Transformation” section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). MISS-tool: -medical image segmentation synthesis tool to emulate segmentation errors. -In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465, -pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

Results -Output

- -

After all -changes have been applied to the mask, by clicking the “Save syn mask & -metrics values” button, two files will be created: 1) the modified mask -will be saved in a PNG file specified by the user, 2) and results -of 24 segmentation metrics between the input truth mask and synthetic masks will -be saved in a CSV file with a name PNG file name_Metrics.csv. -The values of 24 metrics also auto-save in the “Workspace” of MATLAB, it is a -MATLAB struct value named “metrics_value”.

- -

Batch Processing

- -

 “Batch -Processing” button allows you to apply several modifications, including Fourier -Descriptor (FD), Spiculation (SP), and Affine transformation (AF), -to one or many masks all at once.

- -

- -

Note: -1) To apply functions in the “Batch Processing” window, a truth mask must have been -loaded from the main “Segmentation Viewer” window. 2) To click the “Return to -main window” button in main “Segmentation Viewer” window, the “Batch -Processing” window must be closed. 3) The modifications plan (MOD) will be deleted -once the “Batch Processing” window is closed. To keep it, please click “Save to -MOD file”.

- -

Parameters

- -

The -parameters are grouped by the three types of modifications to masks, as shown -in the “Synthesis Tools” before. To look at the details of the parameters for -each type of modifications, please use their links: Fourier Descriptor (FD), Spiculation -(SP), and Affine -transformation (AF).

- -

The -parameters: Detail and Range for Fourier -Descriptor (FD), will be presented and saved in percentage (%) forms. -Where:

- -

- -

- -

The -details about these parameters can be found in Fourier Descriptor (FD).

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- -

Editing -operations for modification plan

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- -

Add operations

- -

- -

1.       -Choose one -operation to be added into the modification plan from the parameters field -(right) by clicking its check box.

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2.       -Edit the -parameters.

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3.       -Click “< -Add” button. The operation will be added in queue with an order number.

- -

Operations -can also add/send into the modification plan from the Fourier Descriptor (FD), -Spiculation (SP), -and Affine transformation (AF) windows by -clicking the “Add params to Batch Processing” button.

- -

 

- -

Remove operations

- -

- -

1.       -Select the -operation to be removed on the Modifications list.

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2.       -Click “Remove” -button. This operation will be removed from the queue. The order numbers of operations -after it will subtract one.

- -

Change operations on the Modifications list

- -

- -

1.       -Select the -operation to be changed on the Modifications list (left).

- -

2.       -Choose one -operation from the parameters field (right) by clicking its check box to substitute -the selected operation (left).

- -

3.       -Click “< -Overwrite” button. The selected operation on the Modifications list -(left) will be substituted by the new operation with checked box from the -parameters field (right).

- -

To -click “Copy >” button, the parameters of selected operation will be -copied to the according parameters field, and the title of operation on the -parameters field (right) will flash for highlighting.

- -

- -

 

- -

Applying -modifications to mask(s)

- -

Click the -“Apply to Dashboard Sample (from original)” button, all operations on -the Modifications list will be applied in their orders to the original mask -loaded from the main window.

- -

Note: -this operation will reset the mask that has been modified by other methods -before.

- -

- -

Click the -“Apply to User Input (many)” button, all operations on the Modifications -list will be applied in their orders to the selected masks by users. And -the modified mask will be saved in PNG files under folder: “…/MISS-tool/Saved/Batch_saved/”, -by adding “_syn” in their file names.

- -

The -supported image formats are: '*.jpg; *.tif; *.png; *.gif'. The image should -have only one object (connected component) as the segmentation or truth mask. -The image will be converted to binary image, of which “1” stands for the object -(foreground) and “0” stands for the background.

- -

 

- -

Save -& load modifications plan

- -

- -

Click the -“Save to MOD file” button, the operations on the Modifications list -will be saved in an XML file: “/MISS-tool/Saved/saved_batch_processing_params.xml”. -To load saved operations into the Modifications list, click “Load -from MOD file” and choose an XML file to open.

- -

Note: -“Load from MOD file” will reset the Modifications list. Unsaved -operations will be lost.

- -

The -Use of Command (Code)

- -

Similar to -the GUI, the Command model has two parts:

- -

·         -Segmentation -performance evaluation for user’s inputs (truth and segmentation results)

- -

·         -Image -segmentation synthesis (truth masks provided by user)

- -

Main -functions

- -

Function for segmentation -evaluation metrics: seg_measures

- -

This -function is used for segmentation performance evaluation of user’s inputs -(truth masks and segmentation results)

- -

Usage: idx = seg_measures(truth_mask, -seg_mask, compute_Hausdorff)

- -

Input:  {truth_mask, -seg_mask}: two binary images {0, 1} for the truth (reference) mask -and segmentation mask; same size

- -

            compute_Hausdorff: if compute Hausdorff -Distances (slower); Boolean.

- -

o   -true: compute HD and AVD

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o   -false: do NOT compute HD and AVD

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Output: A struct data includes 24 -indexes of segmentation evaluation metrics [ref]

- -

1.       -DICE   Dice -Coefficient

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2.       -JAC     Jaccard -Coefficient

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3.       -MSI      Medical -Similarity Index

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4.       -FN       False -Negative (Confusion matrix)

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5.       -FP        False -Positive (Confusion matrix)

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6.       -TN       True -Negative (Confusion matrix)

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7.       -TP        True -Positive (Confusion matrix)

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8.       -TPR     Sensitivity -(Recall, true positive rate)

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9.       -TNR     Specificity -(true negative rate)

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10.   -FPR      Fallout -(false positive rate)

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11.   -PPV     Precision -(also called positive predictive value)

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12.   -ACC    Accuracy

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13.   -AUC    Area -under ROC Curve (one system state)

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14.   -KAP     Cohen -Kappa

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15.   -ARI      Adjusted -Rand Index

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16.   -ICC      Interclass -Correlation

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17.   -VS       Volumetric -Similarity Coefficient

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18.   -MI        Mutual -Information

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19.   -VOI     Variation -of Information

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20.   -PBD     Probabilistic -Distance

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21.   -GCE     Global -Consistency Error

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22.   -HD       Hausdorff -Distance (optionally in voxel or millimeter)

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23.   -AVD    Average -Hausdorff Distance (optionally in voxel or millimeter)

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24.   -MHD   Mahanabolis -Distance

- -

For -example, idx.DICE is the Dice Coefficient of -img1 and img2.

- -

Note: if compute_Hausdorff = false, idx.HD = NaN, idx.AVD = NaN.

- -

References and definitions for -metrics: the 3.MSI is from Kim, Haksoo, et -al. "Quantitative evaluation of image segmentation incorporating medical -consideration functions." Medical physics 42.6Part1 (2015): 3013-3023. https://doi.org/10.1118/1.4921067 , and other metrics are from Taha, -A.A., Hanbury, A. Metrics for evaluating 3D medical image segmentation: -analysis, selection, and tool. BMC Med Imaging 15, 29 (2015). https://doi.org/10.1186/s12880-015-0068-x

- -

Functions for segmentation synthesis

- -

Inputs of -the functions for segmentation synthesis are the image containing a mask to be -modified and several parameters. The image is the key input to these functions, -and it should include only one area (connected component) as the -segmentation/mask to be modified. And the output from a function (modified mask) -is the synthetic segmentation.

- -

Add -spiculations: mod_spike

- -

This -function is used to add spiculations to the mask of user’s input.

- -

Usage: syn = mod_spike(img, center, height, width , -GUI_use)

- -

Input:

- -

·         -img: a binary image {0, 1}

- -

·         -center: location to put the spiculation, -it is an angle value ranging from 0 to 359 degree (360 = 0 degree).

- -

·         -height: the height of the spiculation, -the number of pixels.

- -

·         -width: the width of the spiculation, the -number of pixels.

- -

·         -GUI_use: control the type of input and -output of this function. If true, the input and output are boundaries of the -mask; if false, the input and output are mask images; Boolean.

- -

o   -true: mask boundary -> mask boundary

- -

o   -false: mask image -> mask image

- -

For -technical details of these parameters, please see in the “2.3 -Spiculations” section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). -MISS-tool: medical image segmentation synthesis tool to emulate segmentation -errors. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. -12465, pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

Output: the modified mask (synthetic -segmentation) with an added spiculation, same size as the input image.

- -

Note: -Running this function is to add one spiculation to the input mask at a time. To -add more spiculations to a mask needs to run the function multiple times.

- -

Change contour by Fourier -Descriptor: mod_fourier

- -

This -function is used to change the mask contour of user’s input using Fourier -Descriptors.

- -

Usage: syn = mod_fourier(img, -detail, range, magnitude, GUI_use)

- -

Input:

- -

·         -img: a binary image {0, 1}

- -

·         -detail: the number of Fourier Descriptors -to be not set to zero; the larger value the more detail of the contour -(high-frequency components) will be kept; ranging from 1 to the half total -number of Fourier Descriptors (pixels on the contour).

- -

·         -range: the number of Fourier Descriptors -to be modified; ranging from 0 to (Detail value – 1); the larger value the -greater change is applied to the contour.

- -

·         -magnitude: the magnitude of modification to -the Fourier Descriptors to be changed according to the Range; the larger value -the greater change is applied to the contour.

- -

·         -GUI_use: control the type of input and -output of this function. If true, the input is mask image and output is the -boundary of mask; if false, the input and output are mask images; Boolean.

- -

o   -true: mask image -> mask boundary

- -

o   -false: mask image -> mask image

- -

For -technical details of these parameters, please see in the “2.4 Fourier Descriptor” -section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). -MISS-tool: medical image segmentation synthesis tool to emulate segmentation -errors. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. -12465, pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

Output: A binary image includes the -modified mask (synthetic segmentation) by changing the Fourier Descriptors of -the mask, same size as the input image.

- -

Note: -due to the method of change includes random seeds, for each time running the -function, the result could be different. -For more details about the randomness, please see -in the “2.4 Fourier Descriptor” section of the paper -(https://doi.org/10.1117/12.2653650).

- -

Affine -transformation: mod_affine

- -

This -function is used to change the mask of user’s input using affine -transformations, which includes: resizing (changing the height and width -ratio), shifting, and rotation.

- -

Usage: syn = mod_affine(img, -resize, rotate, shift, GUI_use)

- -

Input:

- -

·         -img: a binary image {0, 1}

- -

·         -resize: the ratio of size change on -x-axis (vertical) and y-axis (horizontal). Input format: [rx, ry].

- -

·         -rotation: the degree of rotation by the center -of the mask; it is an angle value ranging from 0 to 359 degree (360 = 0 -degree).

- -

·         -shift: moving the mask by its center -with the vector (X, Y), the number of pixels. Input format: [sx, sy].

- -

·         -GUI_use: control the type of input and -output of this function. If true, the input is mask image and output is the -boundary of mask; if false, the input and output are mask images; Boolean.

- -

o   -true: mask image -> mask boundary

- -

o   -false: mask image -> mask image

- -

For -technical details of these parameters, please see in the “2.1 Affine -Transformation” section of the paper:

- -

Guan, S., Samala, R. K., Arab, A., & Chen, W. (2023, April). -MISS-tool: medical image segmentation synthesis tool to emulate segmentation -errors. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. -12465, pp. 273-281). SPIE. https://doi.org/10.1117/12.2653650

- -

Output: A binary image includes the -modified mask (synthetic segmentation) by using affine transformations, same -size as the input image.

- -

Note: -the “resize” and “shift” are dual-value parameters requiring two values for -input; the format is like: [x, y].

- -

An -example of segmentation evaluation

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Here we -show an example for calculating the 24 metrics of segmentation images. 10 pairs -of truth mask and segmentation images need to be evaluated. In the same folder -of the MATLAB code, the file name of the i-th truth mask is “TruthMask_i.png”, -and the file name of the i-th segmentation image is “Segmentation_i.png”.

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Implement -by GUI

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1.       -Open Metrics -Computation window (click the “Computing Segmentation Metrics” on the main -options window).

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2.       -Click the -“Segmentation contourS” in the “User Input (Many)” panel to select 10 -segmentation images.

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3.       -Click the -“Reference contourS” in the “User Input (Many)” panel to select the 10 truth -masks.

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4.       -Click the “Performance -Metrics” buttons to start computing the 24 metrics values for each pair of -segmentations and reference masks. Once calculation finished, it will ask you -to save the results in a CSV file by your selection.

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Implement -by Command (Code)

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The MATLAB -code:

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M={};

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for i= 1: 10

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    truth_i = imread(['TruthMask_', num2str(i), '.png']);   

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    seg_i = imread(['Segmentation_', num2str(i), '.png']);   

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    M{i} = seg_measures(truth_i, - seg_i, true);

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end

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The -result M{} is a cell includes 10 struct -data. For example, M{6}.DICE is the Dice -Coefficient of “TruthMask_6.png” and “Segmentation_6.png”.

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An -example of segmentation synthesis

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Here we -show an example to generate 100 synthetic segmentation -images (changing contour by Fourier Descriptor) -from a truth mask. In the same folder of the MATLAB code, the file name of -truth mask is “TruthMask.png”.

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Implement -by GUI

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1.       -Open Synthesis -of Contour window (click the “Synthesis of Segmentation Contours” on the main -options window).

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2.       -Click the “Load -Truth Mask” button to load the truth mask “TruthMask.png”.

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3.       -Click “Fourier” -button to open the Fourier Descriptor window.

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4.       -In the Fourier -Descriptor window, set parameters like detail = 5, range = 3, and -magnitude = 10. Then, click “Apply to original” button to see its modified -result.

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5.       -Since to -generate more than one synthetic segmentation image, we use the Batch -Processing function. Click the “Batch Processing” button in the main -“Segmentation Viewer” window to open the Batch Processing window.

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6.       -Click “Add -params to Batch Processing” on the Fourier Descriptor window, you will see the -three parameters are added into the Modifications list on the Batch Processing -window. Note: on the Batch Processing window, the parameters: -Detail and Range for Fourier Descriptor (FD), will be presented and saved in -percentage (%) forms. Here for more details.

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7.       -Make 100 copies -of the truth mask and save them in a folder, like “TruthMask_copy1.png”, “TruthMask_copy2.png”, -…, “TruthMask_copy100.png”.

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8.       -Click the -“Apply to User Input (many)” button, then select the 100 copies of the truth -mask. Operations on the Modifications list will be applied in their orders to -the selected masks by users. The modified (synthetic) mask will be saved in PNG -files under folder: “…/MISS-tool/Saved/Batch_saved/”, by adding “_syn” in their -file names. Like “TruthMask_copy1_syn.png”, “TruthMask_copy2_syn.png”, …, -“TruthMask_copy100_syn.png”.

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Implement -by Command (Code)

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The MATLAB -code:

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truth_img = imread('TruthMask.png');

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% modify the mask by Fourier - Descriptor; it contains randomness

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detail  - = 5; % < total pixels

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range = 3; % - <= detail

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magnitude = 10; % - >=0

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Syn_masks= cell(100,1);

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for k = 1:100

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Syn_masks{k} = mod_fourier(truth_img, detail, - range, magnitude, false);

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end

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The -result Syn_masks{} is a cell includes 100 segmentation -images. For example, Syn_masks{i} is the i-th -(synthetic) segmentation image.

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