Abstract
A new 3D segmentation method based on the level set technique is proposed. The main contribution is a robust evolutionary model which requires no fine tuning of parameters. A closed 3D surface propagates from an initial position towards the desired region boundaries through an iterative evolution of a specific 4D implicit function. Information about the regions is involved by estimating, at each iteration, parameters of probability density functions. The method can be applied to different kinds of data, e.g for segmenting anatomical structures in 3D magnetic resonance images and angiography. Experimental results of these two types of data are discussed.
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Keywords
- Magnetic Resonance Angiography
- Deformable Model
- Adaptive Segmentation
- Magnetic Resonance Angiography Data
- Stochastic Expectation Maximization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Farag, A., Hassan, H. (2004). Adaptive Segmentation of Multi-modal 3D Data Using Robust Level Set Techniques. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_18
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DOI: https://doi.org/10.1007/978-3-540-30135-6_18
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