Traditional machine learning for limited angle tomography
| Autor: | Yixing Huang, Yanye Lu, Andreas Maier, Guenter Lauritsch, Oliver Taubmann |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Decision tree Health Informatics 02 engineering and technology Machine learning computer.software_genre Health informatics Artifact reduction 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Limited angle tomography business.industry Phantoms Imaging Regression analysis General Medicine 020601 biomedical engineering Computer Graphics and Computer-Aided Design Computer Science Applications Surgery Computer Vision and Pattern Recognition Artificial intelligence business Artifacts Tomography X-Ray Computed computer Algorithms |
| Zdroj: | International journal of computer assisted radiology and surgery. 14(1) |
| ISSN: | 1861-6429 |
| Popis: | The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images.REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp-Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images.REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTree. |
| Databáze: | OpenAIRE |
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