Well, I've finally made my graduation thesis available online. I hope it may help someone :)
The link is here: http://downloads.sourceforge.net/ivussnakes/TG.pdf
The abstract is the following:
This graduation thesis deals with intravascular ultrasound (IVUS) image segmentation through LiveWire and Snakes algorithms. As the type of ultrasound studied is the one from coronaries, there’s an initial detailing of the medical environment in which the problem is inserted.
The introduction is followed by an explanation of how LiveWire technique works, which is by calculating the shortest cost path between two pixels of the image. There’s a discussion about the implementation, using binary trees and a heap data structure. This is followed by the evaluation of which costs should be adopted, which were: modulus and direction of the gradient, the laplacian and a non-linear function.
Then, Snakes algorithm is described, which simulates a set of vertices submitted to one internal ﬁeld of energy and another external one. There’s also a discussion about the number of iterations to be adopted as well as the values used as parameters.
It is explained, so, how simulated images, used for evaluation, were generated. Besides, a model for Speckle noise is described as well as the ﬁlter to which the images were submitted.
There’s a brief discussion on how the algorithms were implemented, with a special focus on ImageJ platform and it’s extensibility features.
The results are, then, described, with each of the simulated image types: noiseless, with Speckle noise and ﬁltered. The main results found are that LiveWire has better segmentation quality and is slightly affected by noise, while Snakes is faster and is practically not aﬀected by wrong initial points retrieved from longitudinal segmentation.
There’s, then, the conclusion, describing what was found, focusing the high velocity of Snakes technique, followed by good results. Although better results were found for LiveWire, the processing time was about 500 times higher, what might indicate this method is not desirable. Due to the good results found with simulated images, the developed platform promises to work efficiently in a medical environment, after submitted to validation by specialists.
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