Abstract：
: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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Amiot C,Girard C,Chanussot J,Pescatore J,Desvignes Mdoi
10.1109/TMI.2014.2349034subject
Has Abstractpub_date
2015-01-01 00:00:00pages
137-47issue
1eissn
0278-0062issn
1558-254Xjournal_volume
34pub_type
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