Curvelet Based Contrast Enhancement in Fluoroscopic Sequences

• Published in: IEEE transactions on medical imaging Vol. 34; no. 1; pp. 137 - 147
• Main Authors: Amiot, C., Girard, C., Chanussot, J., Pescatore, J., Desvignes, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Classification; Computer Science; Constraining; Fluoroscopy - methods; Image edge detection; Image enhancement/restoration; Image Processing; Image Processing, Computer-Assisted - methods; Noise; Noise reduction; Optimization; Radiation Dosage; Shape; Spatial filtering; Temporal logic; Tomography, X-Ray Computed; Transforms; X-ray imaging and computed tomography; X-rays; Curvelets; X-ray imaging and computed tomography; Image enhancement/restoration
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2014.2349034

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.