Multiple-organ segmentation by graph cuts with supervoxel nodes
| Autor: | Hiroshi Ishikawa, Toshiya Takaoka, Yoshihiko Mochizuki |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Segmentation-based object categorization
business.industry Computer science Scale-space segmentation 02 engineering and technology Image segmentation computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Voxel Cut 0202 electrical engineering electronic engineering information engineering Medical imaging 020201 artificial intelligence & image processing Computer vision Segmentation Graphical model Artificial intelligence business computer |
| Zdroj: | MVA |
| DOI: | 10.23919/mva.2017.7986891 |
| Popis: | Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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