COMP5424 Assignment: Medical Image Computing Module Development
In this assignment, you will build a fundamental MIC platform to perform three individual tasks on
the sample data provided and observe the effects achieved by MIE, MIS and MIR respectively. To
complete this assignment, you are expected to use 3D slicer with the programming skills learnt from
this course, based on the skeleton code and sample data provided on Canvas.
For programming related tasks (Task A and Task B), you are suggested to remove all irrelevant
module loading paths before you start to do these tasks. Additionally, please wait for your code
to be processed, your Slice interface may be freezed as it may take some time to run your MIE
and MIS code completely.
Task A: Medical Image Enhancement (MIE)
R Key Information:
• Sample Data: Task A / MRHead.nrrd
• Skeleton Code: Task A / MedicalImageEnhancement.py
• Coding Deliverable: Completed solution code (MedicalImageEnhancement.py)
• Report Deliverable: Corresponding section in the appendix report, including the demon-
stration of the results of 3D convolution performed with the (1) smoothing, (2) sharpening
and (3) edge detection ﬁlters.
You are expected to program an image ﬁltering algorithm with Python, which performs a 3D
convolution on the 3D volume MRHead.nrrd. The ﬁlters to be used, include the smoothing (already
pre-set in the code given), sharpening and edge detection ﬁlters. Please refer to the lecture slides for
the details of the image ﬁltering algorithm.
1. Load data MRHead.nrrd to Slicer.
2. Import the source code ‘MedicalImageEnhancement.py’ to Slicer. Then restart Slicer, and ﬁnd
this ‘Task A – MIE’ module in ‘Assignment’.
3. Open source code ‘MedicalImageEnhancement.py’ and implement your solution. Some
instructions and tips are provided in the source code.
4. After modifying your code, save it and then click on the ‘Reload’ button to reload the module,
so you don’t need to restart Slicer.
5. Change the layout to displace Red Slice only. Superimpose theMRHead ontoMRHead_ﬁltered,
and then change the opacity to see the difference between them.
Task B: Medical Image Segmentation (MIS)
• Sample Data: Task B / MRBrainTumor.nrrd
• Skeleton Code: Task B / MedicalImageSegmentation.py
• Coding Deliverable: Completed solution code (MedicalImageSegmentation.py)
• Report Deliverable: Corresponding section in the appendix report, including (1) the
demonstration of the 3D segmentation result of the tumor, (2) the demonstration of
the experiments on different global and local parameters combinations and (3) the best
global and local parameters found for this segmentation task.
Region-growing algorithm can perform medical image segmentation task via delineating ROIs
iteratively. Recall the dual-checking 2D region growing algorithm from the tutorial content of week
6, this task requires you to further extend the dual-checking region growing algorithm to the 3D
version with Python. Skeleton code and sample data for this task can be found in the ﬁles provided.
1. Load ‘MRBrainTumor.nrrd’ from the ﬁles provided. Use ’Editor’ module to draw a single dot
in the slice which tumor has a clear boundary.
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