Changes after review

Author: sahehb

mevislab.github.io/content/tutorials/image_processing/image_processing6.md

@@ -4,7 +4,7 @@ date: 2025-05-05
 status: "OK"
 draft: false
 weight: 630
-tags: ["Advanced", "Tutorial", "Image Processing", "DICOM", "RTSTRUCT"]
+tags: ["Advanced", "Tutorial", "Image Processing", "DICOM", "RTSTRUCT", "RTDOSE", "RTPLAN"]
 menu: 
   main:
     identifier: "imageprocessing6"
@@ -28,24 +28,30 @@ We use the `ExtractRTStruct`module for this example.
 * **RTDOSE**: Shows planned 3D dose distribution. 
 * **RTPLAN**: Contains treatment plan details like beams and dose settings.
 Together, they ensure accurate and safe radiotherapy delivery.
+
 ## Prepare your network
 First, we need to download the ZIP file at **DICOM patient export**  from:
 
 https://medicalaffairs.varian.com/headandneckbilat-imrtsx2 
 
 It contains the CT, RTSTRUCT, and RTDOSE files needed for this tutorial to work correctly in MeVisLab.
 
+{{<alert class="warning" caption="Attention">}}
+This data is FOR EDUCATIONAL AND SCIENTIFIC EXCHANGE ONLY – NOT FOR SALES OR PROMOTIONAL USE.
+{{</alert>}}
 
-(*Create new folder named* **DICOM_from_Varian** ...*Copy the ZIP file into this folder*).
+Create new folder named *DICOM_FILES*. Extract the ZIP file into this folder.
 
 We will use it in this tutorial. 
 
 Add the module `DicomImport` to your workspace. 
 
 !["DicomImport](/images/tutorials/image_processing/Dicommm.png "DicomImport")
-Then *click Browse and select the new folder named *DICOM_from_Varian* where you copied the ZIP file earlier, then press Import*. You can see it in the below figure:
 
-![Importing DICOM RT Data from the DICOM_from_Varian Folder](/images/tutorials/image_processing/Import.png "Importing DICOM RT Data from the DICOM_from_Varian Folder")
+Then click Browse and select the new folder named *DICOM_FILES* where you copied the ZIP file earlier, then press Import. You can see it in the below figure:
+
+![Importing DICOM RT Data from the DICOM_FILES Folder](/images/tutorials/image_processing/Import.png "Importing DICOM RT Data from the DICOM_FILES Folder")
+
 Now add a `View2D` module and connect it to `DicomImport`. 
 
 As shown in the Data Tree (middle pane), the imported DICOM RT structure includes:
@@ -64,20 +70,17 @@ When you expand the tree view you will see:
 
 After connecting, open the `View2D` and click on each of these items to visualize the corresponding data in the viewer, as shown in the figures below.
 
+{{< imagegallery 4 "/images/tutorials/image_processing/" "RTPLAN" "RTSTRUCT" "CT512" "RTDOSE">}}
 
-{{< imagegallery 4 "/images/tutorials/image_processing/" "RTPLANN" "RTSTRUCTTT" "CT512" "RTDOSEE">}}
-
-
-We select the CT 512×512×272×1 because it’s the foundation for viewing and aligning all other radiotherapy data.
+Select the CT 512×512×272×1 series.
 We now want to view the CT images and the RTSTRUCT data together. The module `DicomImport` only allows to select one single object. In order to select more than one object, we use a `DicomImportExtraOutput` module. Select the CT series in the `DicomImport` module and the RTSTRUCT in the `DicomImportExtraOutput` module.
 
 You have to select the correct index for the RTSTRUCT. In our example it is index 2.
 
-Add `DicomImportExtraOutput`module as shown in the below :
+Add `DicomImportExtraOutput` module as shown in the below :
 
 ![Selecting CT and RTSTRUCT in DicomImport](/images/tutorials/image_processing/SELECTINGCTRTSTRUCT.png "Selecting CT and RTSTRUCT in DicomImport")
 
-
 ### **Visualize RTSTRUCTs as colored CSOs:**
 
 Now we need an `ExtractRTStruct` module to convert RTSTRUCT data into CSOs (Contour Segmentation Objects). CSOs allow MeVisLab to visualize the contours on the CT scan and to interact with them.
@@ -87,15 +90,15 @@ Then coonect it with the `DicomImportExtraOutput` as shown in the figure:
 ![ExtractRTStruct vonverter](/images/tutorials/image_processing/converter.png "ExtractRTStruct converter")
 
 
-Add `SoView2DCSOExtensibleEdito ` to enable visualization and interaction with the CSOs in the 2D viewer, allowing you to edit and work with the contours directly. Connect it with `View2D` and `ExtractRTStruct` modules, as shown, where `View2D` displays the CT scan with the contours.
+Add `SoView2DCSOExtensibleEditor` to enable visualization and interaction with the CSOs in the 2D viewer, allowing you to edit and work with the contours directly. Connect it with `View2D` and `ExtractRTStruct` modules, as shown, where `View2D` displays the CT scan with the contours.
 
 ![Connecting SoView2DCSOExtensibleEditor](/images/tutorials/image_processing/cotour.png "Connecting SoView2DCSOExtensibleEditor")
 
 There are no names for the contours by default, we want to show the names for the contour to identify the segmented structure, so we need the `CSOLabelRenderer` module to add labels (e.g., 'Bladder', 'Prostate') next to each contour, helping to clearly identify the anatomy. The figure below shows that:
 
 ![CSOLabelRenderer](/images/tutorials/image_processing/CSOLabelRenderer.png " CSOLabelRenderer")
 
-As you can see, the contours are labeled with numbers, so we need to display proper names. To do this, open the `CSOLabelRenderer` panel — it will display the panel shown below.
+As you can see, the contours are labeled with numbers. We want to show the names for the contour to identify the segmented structure. To do this, open the `CSOLabelRenderer` panel — it will display the panel shown below.
 
 
 ![CSOLabelRenderer panel](/images/tutorials/image_processing/CSOLabelRendererpanel.png "CSOLabelRenderer panel")
@@ -107,69 +110,60 @@ labelString = cso.getGroupAt(0).getLabel()
 ```
 {{</highlight>}}
 
-Then press apply.
+Then press apply. You can see the names of the structures next to the contours.
 
 ![Edited CSOLabelRenderer Panel](/images/tutorials/image_processing/contournamed.png " Edited CSOLabelRenderer Panel")
 
-
 ### **3D Visualization of Contours Using `SoExaminerViewer`**
 
 If you want to visualize the contours in 3D, follow these steps:
 
-Add the `SoCSO3DRenderer` module and connect it to the `ExtractRTStruc`t module.
-(The `SoCSO3DRenderer` will render the contours (CSOs) in the 3D space).
+Add the `SoCSO3DRenderer` module and connect it to the `ExtractRTStruct` module. The `SoCSO3DRenderer` will render the contours (CSOs) in the 3D space.
 
-Add the `SoExaminerViewer` module and connect it to the `SoCSO3DRenderer` module.
-( The `SoExaminerViewer` will allow you to view the 3D contours. You can rotate, zoom, and move around the 3D image.)
-The following figure is the network and the result : 
-
-![3D Visualization of Contours Using SoExaminerViewe](/images/tutorials/image_processing/3D.png "3D Visualization of Contours Using SoExaminerViewe")
+Add the `SoExaminerViewer` module and connect it to the `SoCSO3DRenderer` module. The `SoExaminerViewer` will allow you to view the 3D contours. You can rotate, zoom, and move around the 3D image.
 
+The following figure shows the network and the result: 
 
+![3D Visualization of Contours Using SoExaminerViewe](/images/tutorials/image_processing/3D.png "3D Visualization of Contours Using SoExaminerViewe")
 
 ### **Visualizing RTDOSE as a Color Overlay Using LUT**
 
-Now we need to add another `DicomImportExtraOutput` module to import multiple DICOM objects ,You have to select the right index for the  example it is index 4 thats mean RTDOSE 199*115*147*1 . Add `MinMaxScan`module to scan the input image and updates the minimum and maximum values of the output image, and connect it with the `DicomImportExtraOutput` 
+Now we need to add another `DicomImportExtraOutput` module to import multiple DICOM objects ,You have to select the right index for the  example it is index 4 thats mean RTDOSE 199*115*147*1 . Add `MinMaxScan` module to scan the input image and updates the minimum and maximum values of the output image, and connect it with the `DicomImportExtraOutput`.
+
 ![Importing RTDOSE Data and Applying MinMaxScan for Image Normalization](/images/tutorials/image_processing/minmaxscan.png "Importing RTDOSE Data and Applying MinMaxScan for Image Normalization")
 
-Add ` Histogram`  module to calculate the image's intensity distribution, and connect it with `MinMaxScan`.
+Add `Histogram` module to calculate the image's intensity distribution, and connect it with `MinMaxScan`.
 
-The `Histogram` module computes the image's intensity distribution, and is connected to the `SoLUTEditor` to modify the lookup table (LUT). The LUT is then passed to the `SoGroup`, which is connected to the `SoView2DOverlay` to blend the 2D image overlay in a 2D viewer. The `SoLUTEditor` allows interactive editing of the LUT, while `SoView2DOverlay` facilitates overlaying the modified image in a 2D scene. Note that `SoView2DOverlay` is for 2D blending, and `GVROrthoOverlay` should be used for OrthoView2D.
- The below figure is shows connnection of the network:
+The `Histogram` module computes the image's intensity distribution, and is connected to the `SoLUTEditor` module to modify the lookup table (LUT). The LUT is then passed to the `SoGroup` module, which is connected to the `SoView2DOverlay` module to blend the 2D image overlay in a 2D viewer. The `SoLUTEditor` module allows interactive editing of the LUT, while `SoView2DOverlay` module facilitates overlaying the modified image in a 2D scene.
 
-![Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing"](/images/tutorials/image_processing/net.png "Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing")
+Note that the `SoView2DOverlay` module is for 2D blending, and the `GVROrthoOverlay` module should be used for `OrthoView2D`.
 
+The below figure is shows connnection of the network:
 
-Now we should update the `Histogram` by pressing the update button on it.
+![Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing"](/images/tutorials/image_processing/net.png "Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing")
 
+Now we should update the `Histogram` module by pressing the update button on it.
 
 ![Update Histogram](/images/tutorials/image_processing/histo.png " Update Histogram")
 
-
-Now Open `SoLUTEditor`, go to ... *Range* ... *Update Range From Histogram* to apply the histogram values, as shown in the figure :
+Now Open `SoLUTEditor` module panel, go to *Range*, *Update Range From Histogram* to apply the histogram values, as shown in the figure:
 
 ![SoLUTEditor Panel](/images/tutorials/image_processing/solut.png "SoLUTEditor Panel")
 
-Then in the same panel from (*Editor*... *change the color of the values*) , as shown in the figure: 
-
+Then in the same panel from (*Editor*... *change the color of the values*), as shown in the figure: 
 
 ![SoLUTEditor Panel Editior](/images/tutorials/image_processing/editior.png " SoLUTEditor Panel Editior")
 
-
 Finally, when you press `View2D `, it will display a 2D anatomical image with a colored RTDOSE overlay, where the dose distribution is visualized using a customized Lookup Table (LUT) that clearly highlights the radiation intensity levels within the body.
 
 ![Visualizing RTDOSE as a Color Overlay Using LUT](/images/tutorials/image_processing/2df.png "Visualizing RTDOSE as a Color Overlay Using LUT")
 
-
----
-
 ## **Summary**
-
 * Load DICOM RT data including RTDOSE and RTSTRUCT .
 * Visualize RTSTRUCTs with `ExtractRTStruct` and `CSOLabelRenderer`.
 * Display contour labels using `CSOLabelRenderer`.
 * View images in 2D (`View2D`) and 3D (`SoExaminerViewer`).
 * Visualize RTDOSE as a color overlay using `SoLUTEditor`.
 * Adjust dose intensity with `Histogram` and `MinMaxScan`.
 
-{{< networkfile "/examples/image_processing/example6/DICOMRT.mlab" >}}
+{{< networkfile "/examples/image_processing/example6/DICOMRT.mlab" >}}