Image Processing

Chapter IV: Image Processing

Image Processing in MeVisLab

Digital image processing is the use of a digital computer to process digital images through an algorithm (see Wikipedia).

MeVisLab provides multiple modules for image processing tasks, such as:

• Filters
• Masks
• Transformations
• Arithmetics
• Statistics

For details about Image Processing in MeVisLab, see the MeVisLab Documentation

In this chapter, you will find some examples for different types of image processing in MeVisLab.

Example 1: Arithmetic operations on two images

Example 1: Arithmetic operations on two images

     This example is also available on YouTube.

Introduction

We are using the Arithmetic2 module to apply basic scalar functions on two images. The module provides 2 inputs for images and 1 output image for the result.

Steps to do

Develop your network

Add two LocalImage modules to your workspace for the input images. Select $(DemoDataPath)/BrainMultiModal/ProbandT1.dcm and $(DemoDataPath)/BrainMultiModal/ProbandT2.dcm from MeVisLab demo data and add a SynchroView2D to your network.

Example 2: Masking images

Example 2: Masking images

     This example is also available on YouTube.

Introduction

The background of medical images is black for most cases. In case an image is inverted or window/level values are adapted, these black pixels outside clinical relevant pixels might become very bright or even white.

Being in a dark room using a large screen, the user might be blended by these large white regions.

Image masking is a very good way to select a defined region where image processing shall be applied. A mask allows to define a region (the masked region) to allow image modifications whereas pixels outside the mask remain unchanged.

Example 3: Region Growing

Example 3: Region Growing

     This example is also available on YouTube.

Introduction

A very simple approach to segment parts of an image is the region growing method. A general explanation can be found here.

In this example, you will segment the brain of an image and show the segmentation results as an overlay on the original image.

Steps to do

Develop your network

Add a LocalImage module to your workspace and select load $(DemoDataPath)/BrainMultiModal/ProbandT1.dcm. Add a View2D module and connect both as seen below.

Example 4: Subtract 3D objects

Example 4: Subtract 3D objects

     This example is also available on YouTube.

Introduction

In this example, we load an image and render it as WEMIsoSurface. Then we create a 3-dimensional SoSphere and subtract the sphere from the initial WEM.

Steps to do

Develop your network

Add a LocalImage module to your workspace and select load $(DemoDataPath)/BrainMultiModal/ProbandT1.dcm. Add a WEMIsoSurface, a SoWEMRenderer, a SoBackground and a SoExaminerViewer module and connect them as seen below. Make sure to configure the WEMIsoSurface to use a Iso Min. Value of 420 and a Voxel Sampling 1.

Example 5: Clip Planes

Example 5: Clip Planes

     This example is also available on YouTube.

Introduction

In this example, we are using the SoGVRDrawOnPlane module to define the currently visible slice from a 2D view as a clip plane in 3D.

Steps to do

Develop your network

First we need to develop the network to scroll through the slices. Add a LocalImage module to your workspace and select the file ProbandT1 from MeVisLab demo data.

Example 6: DICOM RT Visualization in MeVisLab – RTSTRUCT and RTDOSE Workflow

Example 6: DICOM RT Visualization in MeVisLab – RTSTRUCT and RTDOSE Workflow

Introduction

This tutorial explains how to load and visualize DICOM RT (Radiotherapy) data in MeVisLab. You will learn how to:

• Load CT and related RTSTRUCT data.
• Visualize RTSTRUCTs as colored CSOs.
• Show labels next to each RTSTRUCT contour.
• Visualize RTDOSE as a semi-transparent colored overlay.

DICOM RT files are essential in radiotherapy treatment planning.

They include:

• RT Structure Set, containing information related to patient anatomy, for example structures, markers, and isocenters. These entities are typically identified on devices such as CT scanners, physical or virtual simulation workstations, or treatment planning systems.
• RT Plan, containing geometric and dosimetric data specifying a course of external beam and/or brachytherapy treatment, for example beam angles, collimator openings, beam modifiers, and brachytherapy channel and source specifications. The RT Plan entity may be created by a simulation workstation, and subsequently enriched by a treatment planning system before being passed on to a record and verify system or treatment device. An instance of the RT Plan object usually references an RT Structure Set instance to define a coordinate system and set of patient structures.
• RT Dose, containing dose data generated by a treatment planning system in one or more of several formats: three-dimensional dose data, isodose curves, DVHs, or dose points.

Additional objects not used in this tutorial are: