Active geometric model : multi-compartment model-based segmentation & registration
| Autor: | Mukherjee, Prateep |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Multi-compartment Image Registration & Segmentation
Motor Neurons Drosophila Henle's Fiber Layer Computer vision Research Analysis Image processing Digital techniques Mathematical models Image segmentation Image analysis Image registration Optical pattern recognition Diagnostic imaging Data processing Drosophila Imaging Retina Motor neurons Three-dimensional imaging Optical tomography Computer graphics Imaging systems in medicine Computer vision in medicine |
| Druh dokumentu: | Diplomová práce |
| DOI: | 10.7912/C2/2318 |
| Popis: | Indiana University-Purdue University Indianapolis (IUPUI) We present a novel, variational and statistical approach for model-based segmentation. Our model generalizes the Chan-Vese model, proposed for concurrent segmentation of multiple objects embedded in the same image domain. We also propose a novel shape descriptor, namely the Multi-Compartment Distance Functions or mcdf. Our proposed framework for segmentation is two-fold: first, several training samples distributed across various classes are registered onto a common frame of reference; then, we use a variational method similar to Active Shape Models (or ASMs) to generate an average shape model and hence use the latter to partition new images. The key advantages of such a framework is: (i) landmark-free automated shape training; (ii) strict shape constrained model to fit test data. Our model can naturally deal with shapes of arbitrary dimension and topology(closed/open curves). We term our model Active Geometric Model, since it focuses on segmentation of geometric shapes. We demonstrate the power of the proposed framework in two important medical applications: one for morphology estimation of 3D Motor Neuron compartments, another for thickness estimation of Henle's Fiber Layer in the retina. We also compare the qualitative and quantitative performance of our method with that of several other state-of-the-art segmentation methods. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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