Curvelet Based Contrast Enhancement in Fluoroscopic Sequences
| Autor: | Jérémie Pescatore, Jocelyn Chanussot, Carole Amiot, Michel Desvignes, C. Girard |
|---|---|
| Přispěvatelé: | Thales Electron Devices, GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Noise reduction
02 engineering and technology Radiation Dosage 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Curvelet Image Processing Computer-Assisted X-ray imaging and computed tomography Computer vision Electrical and Electronic Engineering Mathematics Image enhancement/restoration Sequence Radiological and Ultrasound Technology Spatial filter business.industry Contrast (statistics) Computer Science Applications Visualization Curvelets Fluoroscopy [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 020201 artificial intelligence & image processing Artificial intelligence Noise (video) business Tomography X-Ray Computed Software Algorithms |
| Zdroj: | IEEE Transactions on Medical Imaging IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2015, 34 (1), pp.137-147. ⟨10.1109/TMI.2014.2349034⟩ |
| ISSN: | 0278-0062 |
| DOI: | 10.1109/TMI.2014.2349034⟩ |
| Popis: | International audience; —Image guided interventions have seen growing interest in recent years. The use of X-rays for the procedure impels limiting the dose over time. Image sequences obtained thereby exhibit high levels of noise and very low contrasts. Hence, the development of efficient methods to enable optimal visualization of these sequences is crucial. We propose an original denoising method based on the curvelet transform. First, we apply a recursive temporal filter to the curvelet coefficients. As some residual noise remains, a spatial filtering is performed in the second step, which uses a magnitude-based classification and a contextual comparison of curvelet coefficients. This procedure allows to denoise the sequence while preserving low-contrasted structures, but does not improve their contrast. Finally, a third step is carried out to enhance the features of interest. For this, we propose a line enhancement technique in the curvelet domain. Indeed, thin structures are sparsely represented in that domain, allowing a fast and efficient detection. Quantitative and qualitative evaluations performed on synthetic and real low-dose sequences demonstrate that the proposed method enables a 50% dose reduction. |
| Databáze: | OpenAIRE |
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