I addressed the segmentation and the tracking tasks separately. For the segmentation, which is particularly challenging because of the presence of clutter, the low signal to noise ratio and the presence of multiple structures in the images, I finally combined several layers of signal processing, clustering, and optimization methods known for their robustness. For the tracking, I developed a hybrid approach achieving better performances by combining geometric and iconic clues.

- a guidewire detector was applied to the fluoroscopic images to enhance its pixels
- the pixels passing a threshold were extracted and clustered according to the local orientation of the detected structures
- a line segment was fitted to each cluster using a variant of RANSAC, referred as MSAC, known for its robustness to outliers
- the line segments were joined to reconstruct the wires by solving a discrete optimization problem via local search

[A] |
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[B] |
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[C] |
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[D] |
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Guidewires segmentation. [A] fluoroscopic image [B] guidewire detector output [C] extracted segments [D] final segmentation. |

**Honnorat N**, Vaillant R, Paragios N.Graph-based Guide-wire Segmentation through Fusion of Contrast-enhanced and fluoroscopic Images.

__IEEE International Symposium on Biomedical Imaging (ISBI)__**2012**948-51**Honnorat N**, Vaillant R, Duncan JS, Paragios N.Curvilinear structures extraction in cluttered bioimaging data with discrete optimization methods.

__IEEE International Symposium on Biomedical Imaging (ISBI)__**2011**1353-1357**Honnorat N**, Vaillant R, Paragios N.Guide-wire extraction through perceptual organization of local segments in fluoroscopic images.

__International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)__**2010**13(Pt 3):440-8**Honnorat N**, Vaillant R, Paragios N.Robust guidewire segmentation through boosting, clustering and linear programming.

__IEEE International Symposium on Biomedical Imaging (ISBI)__**2010**924-927

The optimal snake tracker displacements were determined by solving a Markov Random Field. The most important contribution of this work resides in the combination of geometric potentials constraining the global shape of the guidewire with iconic potentials integrated along the B-spline snake tracker. The iconic potentials were significantly improved by exploiting guidewire orientation estimation provided by the steerable filters developed for their segmentation.

$E_{MRF}=\mu_{iconic} \left(\sum_{k\in{\{i,j\}}}{V_{k}^{iconic}(l_k)}+\sum_{k,r\in{\{i,j\}}}{V_{k,r}^{iconic}(l_k,l_r)}\right)+$

$ ~~~~~~~~~~\mu_{landmarks} \left(\sum_{k\in{\{i,j\}}}{V_{k}^{landmarks}(l_k)}+\sum_{k,r\in{\{i,j\}}}{V_{k,r}^{landmarks}(l_k,l_r)}\right)+$

$ ~~~~~~~~~~\mu_{consistency} \left(\sum_{k\in{\{i,j\}}}{V_{k}^{consistency}(l_k)}+\sum_{k,r\in{\{i,j\}}}{V_{k,r}^{consistency}(l_k,l_r)}\right)+$

$ ~~~~~~~~~~\mu_{coupling} \left(\sum_{k\in{\{i,j\}}}{V_{k}^{coupling}(l_k)}+\sum_{k,r\in{\{i,j\}}}{V_{k,r}^{coupling}(l_k,l_r)}\right)+$

$ ~~~~~~~~~~\mu_{rigidity} \left(\sum_{k\in{\{i,j\}}}{V_{k}^{rigidity}(l_k)}+\sum_{k,r\in{\{i,j\}}}{V_{k,r}^{rigidity}(l_k,l_r)}\right)$

iconic term |
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landmark matching |
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dynamic consistency |
penalizes local deforma- tions w.r.t. model $T$ updated by exponential forgetting: |
$T(s) \leftarrow \frac{memory-1}{memory}T(s)+\frac{1}{memory}C(s)$ |

coupling |
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rigidity |

**Honnorat N**, Vaillant R, Paragios N.Graph-based geometric-iconic guide-wire tracking.

__International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)__**2011**14(Pt 1):9-16