Segmentation of female pelvic organs in axial magnetic resonance images using coupled geometric deformable models
| Autor: | João Manuel R. S. Tavares, Zhen Ma, T. Mascarenhas, Renato Natal Jorge |
|---|---|
| Přispěvatelé: | Faculdade de Medicina, Faculdade de Engenharia |
| Rok vydání: | 2013 |
| Předmět: |
Female pelvic cavity
Computer science Urinary Bladder Normal Distribution Partial volume Health Informatics Level set Technological sciences Biological sciences Image Processing Computer-Assisted medicine Humans Segmentation Computer vision Biological sciences [Natural sciences] medicine.diagnostic_test business.industry Rectum Magnetic resonance imaging Image segmentation Models Theoretical Magnetic Resonance Imaging Computer Science Applications Vagina Female pelvic organs Ciências Tecnológicas Ciências biológicas Female Noise (video) Artificial intelligence Ciências biológicas [Ciências exactas e naturais] business Algorithms Software |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 0010-4825 |
| DOI: | 10.1016/j.compbiomed.2012.12.012 |
| Popis: | The segmentation of pelvic structures in magnetic resonance (MR) images of the female pelvic cavity is a challenging task. This paper proposes the use of three novel geometric deformable models to segment the bladder, vagina and rectum in axial MR images. The different imaging appearances and prior shape knowledge are combined into a level set framework as segmentation cues. The movements of the contours are coupled with each other based on interactive information, and the organ boundaries can be segmented simultaneously. With the region-based external forces defined, the proposed algorithms are robust against noise and partial volume effect. |
| Databáze: | OpenAIRE |
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