Topics:
Download the MITK Workbench application. Load the testing image lowdose_CT.nii.gz (you will get an e-mail with a download link). If the data is too large for your PC to process, use optionally the smaller lowdose_CT_cropped.nii.gz file. After the file is opened, the image's name will appear as entry in the Data Manager view, you will also see four rendering windows, they show the standard anatomical planes and a 3D-visualization of the image. The Level Window slider appears on the right.
UL Threshold
segmentation tool from the 3D Tools
pane. Select a lower and upper threshold and create a segmentation. Create a surface representation (right-click on the segmentation node in the DataManager opens a context menu, choose the entry Create polygon model
) and make a screenshot for the report. Comment on your choice of thresholds used for segmentation
Create new segmentation
in the upper part of the segmentation plugin, the tools will be active afterwards
Save
segmentation image from Data Manager) and upload it together with your report (the segmentation image should not be larger than 10MB).
Segmentation You have experienced, how tedious a manual segmentation can be, which is one of the main motivations for development of (semi-)automated segmentation methods. In this part, we will look at segmentation by means of pixel classification. It is about finding set of values (features) for each pixel, that allows to construct a criterion which assigns a class label to each pixel. The threshold-based segmentation from first part is a simple example of this principle. We have the pixel value $I(x,y)$ as feature and define the criterion for the bone class as $\theta_l \leq I(x, y) \leq \theta_u$.
In this task, we will look for a larger set of features for each pixel, that will lead to a criterion for classes bone, muscle, fat and background.
Pixel classification
[2 pts] Homework task
Download the ilastik application. Download the input data archive cv03_ctsegm.zip
In ilastik:
training
folder within the data archive.