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Recalage de groupes d’images médicales 3D par extraction de points d’intérêt

Abstract : The ever-increasing amount of medical images stored in hospitals offers a great opportunity for big data analysis. In order to pave the way for huge image groups screening, we need to develop methods able to make images databases consistent by group registering those images. Currently, group registration methods generally use dense, voxel-based, representations for images and often pick a reference to register images. We propose a group registration framework, without reference image, by using only interest points (Surf3D), able to register hundreds of medical images. We formulate a global problem based on interest point matching. The inter-patient variability is high, and the outliers ratio can be large (70\%). We pay a particular attention on inhibiting outliers contribution. Our first contribution is a two-step rigid groupwise registration. In the first step, we compute the pairwise rigid registration of each image pair. In a second step, a complete graph of those registrations allows us to formulate a global problem using the laplacian operator. We show experimental results for groups of up to 400 CT-scanner 3D heterogeneous images highlighting the robustness and speed of our approach. In our second contribution, we compute a non-rigid groupwise registration. Our approach involves half-transforms, parametrized by a b-spline pyramid, between each image and a common space. A reference dataset shows that our algorithm provides competitive results while being much faster than previous methods. Those results show the potential of our interest point based registration method for huge datasets of 3D medical images. We also provide to promising perspectives: multi-atlas based segmentation and anthropology.
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Rémi Agier. Recalage de groupes d’images médicales 3D par extraction de points d’intérêt. Imagerie médicale. Université de Lyon, 2017. Français. ⟨NNT : 2017LYSEI093⟩. ⟨tel-02004354⟩

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