A Parallel Method for Anatomical Structure Segmentation based on 3D Seeded Region Growing
| Autor: | J. R. Gonzalez, Nazareth N. Rocha, Aura Conci, Esteban Clua, Flavio Luiz Seixas, Paulo Lacerda, Célio Albuquerque |
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| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Computer science business.industry 0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation 020601 biomedical engineering 030218 nuclear medicine & medical imaging 03 medical and health sciences Identification (information) 0302 clinical medicine Region growing Segmentation Artificial intelligence Tomography business |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9206630 |
| Popis: | Medical images are important elements for the diagnosis of diseases. Computer Aided Diagnostic has evolved in recent years along with the processing capacity of computers as well as the emergence of new computational techniques. Segmentation is a valuable approach for identifying a specific area in human body images, such as the lungs and heart. This work proposes an algorithm to segment anatomical structures using parallel 3D region growing. Experiments using different Computer Tomography scans show that the proposed approach can run 150 times faster than the typical sequential region growing algorithm while providing good results in the identification of the target region. |
| Databáze: | OpenAIRE |
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