I have not been the most efficient person for the past two weeks. I think I have been thinking too much about other things. I am finishing up my MIUA paper final revision which was accepted for an oral presentation for the upcoming MIUA conference .
For the past week, amongst other things (setting up cricket matches), I have been looking at various vessel analysis techniques especially R. Manniesing's PhD thesis titled "Image analysis in CT Angiography". Here I was stomped by some unfamiliar techniques such as diffusion filters and level set methods.
Diffusion filters are governed by diffusion equations which basically control the amount of diffusion when applying the Gaussian filter to vessel boundaries. The main idea is to come up with a non-linear diffusion co-efficient (a.k.a conductivity coeff.) which stops diffusion at the vessel boundaries thereby preserving the vessel topology. I was interested in exploring this area since I was a little concerned about the amount of noise in some of my new MRI datasets. The application of the Gaussian blur to the images with low variance has actually not done much harm to the pulmonary veins and its branches. Here are some images to prove that in atleast one of the datasets:
For the past week, amongst other things (setting up cricket matches), I have been looking at various vessel analysis techniques especially R. Manniesing's PhD thesis titled "Image analysis in CT Angiography". Here I was stomped by some unfamiliar techniques such as diffusion filters and level set methods.
Diffusion filters are governed by diffusion equations which basically control the amount of diffusion when applying the Gaussian filter to vessel boundaries. The main idea is to come up with a non-linear diffusion co-efficient (a.k.a conductivity coeff.) which stops diffusion at the vessel boundaries thereby preserving the vessel topology. I was interested in exploring this area since I was a little concerned about the amount of noise in some of my new MRI datasets. The application of the Gaussian blur to the images with low variance has actually not done much harm to the pulmonary veins and its branches. Here are some images to prove that in atleast one of the datasets:
And their surface reconstructions show how easy it is to work with the Gaussian blurred image (bottom image):
So I was quite convinced that the Gaussian blur was rather a blessing than a curse. I thought perhaps I should apply it to my previous MRI studies (which are not as noisy as the new sets) and improve the segmentation results.
I diverted my attention by the middle of the week, away from noise-removal to vessel-axis tracking. I felt that this was prime to two things: 1) Eventually enabling the separation of the pulmonary vein from the artery. 2) Eventually being able to determine the number of pulmonary veins to an atrium. Manniesing's PhD thesis gave a very interesting insight into how Level set methods along with prior vessel topology information can be used to segment the vessel-axis. I have been off-beat with Level set methods and spent the remaining week listening to Level set methods lecture from MIT's opencourseware lecture series for their Mathematical Methods for Engineer's course run by R. Strang. A link to the entire series can be found here.
After meditating on a half-an-hour tube ride, I am a little convinced that given that the vessel-axis of the pulmonary vein and the artery can be tracked, they can be separated trivially. However, what worries me most is that it won't be as trivial to obtain the vessel axes at the vein-artery junctions.
off note, I feel compelled to say that I have started taking some interest in world history.
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