A SHAPE-TEMPLATE BASED TWO-STAGE CORPUS CALLOSUM SEGMENTATION TECHNIQUE FOR SAGITTAL PLANE T1-WEIGHTED BRAIN MAGNETIC RESONANCE IMAGES
| Autor: | Jayanth Krishna Mogali, Naren Nallapareddy, Michael Unser, Chandra Sekhar Seelamantula |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Initialization Scale-space segmentation Pattern recognition Image segmentation Curvature Sagittal plane medicine.anatomical_structure Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition medicine Segmentation Computer vision Artificial intelligence Affine transformation business Electrical Engineering Mathematics |
| Zdroj: | ICIP |
| ISSN: | 2381-3652 |
| Popis: | We propose a semi-automatic technique to segment corpus callosum (CC) using a two-stage snake formulation: A restricted affine transform (RAT) constrained snake followed by an unconstrained snake in an iterative fashion. A statistical model is developed to capture the shape variations of CC from a training set, which restrict the unconstrained snake to lie in the shape-space of CC. The geometry of the constrained snake is optimized using a local contrast-based energy over RAT space (which allows for five degrees of freedom). On the other hand, the unconstrained snake is optimized using a unified energy (region, gradient, and curvature energy) formulation. Joint optimization resulted in increased robustness to initialization as well as fast and accurate segmentation. The technique was validated on 243 images taken from the OASIS database and performance was quantified using Jaccard's distance, sensitivity, and specificity as the metrics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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