Carole Amiot

Curvelet Based Contrast Enhancement in Fluoroscopic Sequences

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.

Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence

In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction.

Contextual filtering in curvelet domain for fluoroscopic sequences

X-ray exposure during image guided interventions can be important for the patient as well as for the medical staff. Therefore dose reduction is a major concern. Nevertheless, decreasing the dose per image affects significantly the image quality. As a matter of fact, this tends to increase the noise and reduce the contrast. Hence, we propose a new and efficient method to reduce the noise in low dose fluoroscopic sequences. Many studies in that domain have been proposed implementing either multi-scale approaches using wavelet with its derivatives or using filters in the direct space. Our work is based on a spatio-temporal denoising filter using the curvelet transform. Indeed, this sparse transform represents well smooth images with edges and can be applied to fluoroscopic images in order to achieve robust denoising performances. Therefore, we propose to combine a temporal recursive filter with a spatial curvelet filter. Our work is focused on the use of the statistical dependencies between the curvelet coefficients in order to optimize the threshold function. Determining the correlation among coefficients allows to detect which coefficients represent the relevant signal. Thus, our method allows to diminish or even to erase curvelet-like artefacts. The performances and robustness of the proposed method are assessed both on synthetic and real low dose sequences (ie: 20 nGy/frame).

Image denoising using contextual modeling of curvelet coefficients

We propose an image denoising method which takes curvelet domain inter-scale, inter-location and inter-orientation dependencies into account in a maximum a posteriori labeling of the curvelet coefficients of a noisy image. The rationale is that generalized neighborhoods of curvelet coefficients contain more reliable information on the true image than individual coefficients. Based on the labeling of coefficients and their magnitudes, a smooth thresholding functional produces denoised coefficients from which the denoised image is reconstructed. We also outline a faster approach to labeling and thresholding, relying on contextual comparisons of coefficients. Quantitative and qualitative evaluations on natural and X-ray images show that our method outperforms related multiscale approaches and compares favorably to the state-of-art BM3D method on X-ray data while executing faster.

Spatio-temporal cellular automata-based filtering for image sequence denoising: Application to fluoroscopic sequences

This work presents a novel spatio-temporal cellular automata-based filtering (STCAF) for image sequence denoising. Most of the methods using cellular automata (CA) for image denoising involve the manual design of the rules that define the behaviour of the automata. This is a complex and not straightforward operation. In order to tackle this problem, this paper proposes to use evolutionary methods to obtain the CA set of rules which produces the best possible denoising under different noise models or/and image sources. This is implemented using a spatio-temporal neighbourhood for each pixel, which significantly improves the results with respect to simple spatio or temporal set of neighbours. The proposed method is tested to reduce the noise in low-dose X-ray image sequences. These data have a severe signal-dependent noise that must be reduced avoiding artifacts while preserving structures of interest for a medical inspection. The proposed method outperforms several state-of-the-art algorithms on both simulated and real sequences.

Fluorosocopic sequence denoising using a motion compensated multi-scale temporal filtering.

We propose a temporal motion-compensated filter operating on dual-tree complex wavelet coefficients to denoise low-dose X-ray sequences. This filter allows for a great noise reduction while preserving moving objects and structures. We take advantage of the properties of multi-scale spaces to perform a fast and robust motion tracking. The result of this step is then used in the temporal filter for denoising purpose. Quantitative and qualitative analysis on real fluoroscopic sequences show that our method outperforms state-of-the-art VBM3D method and allows for a dose reduction as high as 80%.