Robust Unmixing of Dynamic Sequences Using Regions of Interest
Autor: | Michel Desvignes, Marc Filippi, Eric Moisan |
---|---|
Přispěvatelé: | GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) |
Rok vydání: | 2017 |
Předmět: |
Inverse methods
Planar Imaging Underdetermined system Linear programming Computer science Partial volume 02 engineering and technology Computer-aided detection and diagnosis Kidney Blind signal separation 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences 0302 clinical medicine [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Robustness (computer science) Image Interpretation Computer-Assisted [INFO.INFO-IM]Computer Science [cs]/Medical Imaging 0202 electrical engineering electronic engineering information engineering Source separation Humans Electrical and Electronic Engineering Radionuclide Imaging Radiological and Ultrasound Technology business.industry Pattern recognition Computer Science Applications Distance matrix Blind source separation 020201 artificial intelligence & image processing Artificial intelligence Tomography business Nuclear imaging Radioisotope Renography Software Algorithms |
Zdroj: | IEEE Transactions on Medical Imaging IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2018, 37 (1), pp.306-315. ⟨10.1109/TMI.2017.2759661⟩ |
ISSN: | 1558-254X 0278-0062 |
DOI: | 10.1109/TMI.2017.2759661⟩ |
Popis: | International audience; In dynamic planar imaging, extraction of signals specific to structures is complicated by structures superposition. Due to overlapping, signals extraction with classic regions of interest (ROIs) methods suffers from inaccuracy, as extracted signals are a mixture of targeted signals. Partial volume effect raises the same issue in dynamic tomography. Source separation methods such as factor analysis of dynamic sequences, have been developped to unmix such data. However the underlying problem is underdetermined and the model used is not relevant in the whole image. This non-uniqueness issue was overcome by introducing prior knowledge, such as sparsity or smoothness, in the separation model. In pratice, these methods are barely used because of the lack of reliability of their results. Previously developed methods aimed to be fully automatic, but efficiency can be improved with additional prior knowledge. Some methods using ROIs knowledge in a straightforward way have been proposed. In this paper, we propose an unmixing method, based on an objective function minimization and integrating these ROIs in a different and robust manner. The objective function promotes consistent solutions regarding ROIs while relaxing the model outside ROIs. In order to reduce user-dependent effects, ROIs are used as soft constraints in a robust way through the use of a distance matrix. Consistency, effectiveness and robustness to the ROIs selection are demonstrated on a toy example, a highly realistic simulated renography dataset and a clinical dataset. Performance is compared with competitive methods. |
Databáze: | OpenAIRE |
Externí odkaz: |