Natural interactions for medical image analysis

Interaction-Design Master Thesis

Visualization of medical Scans

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Today I tried to get some more information on Medical Data Visualization. There is a lot of very interesting looking visualizations out there. But as I discussed with my mentor Mine, it’s always nice to have nice looking visualizations especially for us designers, but the more important questions should be if it is helpful in any way for the Radiologists to read the data.

Researching for this post reminded me of the Bodies Exhibition I visited a while ago. I am not sure if the Bodies Exhibition can be titled as art, but I think it could be very inspiring for this project, because its displaying human bodies and its parts in a lot of different and interesting ways. Slicing the human body and extracting special organs is exactly what I found in Radiology.

1. picture shows a full human body but only the blood vessels.
2. sliced up human body
3. human skull with arteries and veins in different colors


Following you’ll find a collection of different ways to visualize medical scans:

High Contrast 2D

this is probably the most used visualization today. There are special (and very expensive) greyscale screens to display high contrast 2D images. Normally Radiologists use two of these screens side by side. With CT scans you can get a full body scan in slices, so you can “move” through the body by viewing slices from different parts of the body.

Thinking about these special screens, I get the idea why it is so important to have the right lighting in a Reading Room.


Advanced 3D

I found this company using game-graphic-engines to visualize medical images in 3D. I am not sure if that’s the right way to go. How complex textures and shaders are useful in viewing medical images? Of course it looks fancy and might be a good thing for advertising a product.


Real 3D

I even found a company that supports 3D anaglyphic (with 3D glasses) viewing. I can imagine this to be more helpful, especially for surgeons. From what I know surgeons are much more confident with 3D data. Radiologists instead are mainly trained to work with 2D images. VR (virtual reality) and AR (augmented reality) are some interesting topics for further exploration.


3D + time = 4D

Xromm 4D is currently under development. from what I understand its basically a 3D scan over time. So you can view/scan movements from different directions. In this video example they show 3D bones inside a pig while it is walking. another example for 4D imaging is a 3D ultrasound, where 3D images are rapidly captured and animated to produce a 4D ultrasound.

xromm 4d

Feature (e.g. Organs) Recognition and Coloring

automatic feature recognition makes it easy to distinguish between different organs and bones. you can easiely turn off different body parts to view specific organs. in the picture below you can see that the organs are colored (e.g. bones are blue) and some are transparent (e.g. skin). are there other ways to visualize different organs than coloring them?


what is 5D data?

Upper row: fused image of nuclear medicine acquisition and MR acquisition
Middle row
: MR study yields excellent anatomic detail (spatial resolution)
Bottom row
: nuclear medicine (SPECT) images yield excellent functional information


I the upper row you can see a merged image of a MRI scan and a SPECT image. As described above MRI scans are great for showing anatomic details, especially for soft tissues. SPECT images instead are showing the activity of organs with the help of some radioactive tracers that are injected into the patient. If you imagine this taken in real time (like a video) and in 3D you could say its a 5D image. I have seen a couple of companies talking about 5D images, but they would never explain what 5D actually is. my explanation for 5D: 3 dimensions of space, 1 dimension of time, 1 dimension of organs activity. Not sure if that is right… but at least I like the idea of this very interlaced data.


Written by Jannes

January 26, 2009 at 18:32

Posted in Phase 01 - Research

Tagged with , , , , ,

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