Multimodal Image Registration Applied to Magnetic Resonance and Ultrasound Prostatic Images
Résumé
This thesis investigates the employment of different deformable registration tech-niques to register pre-operative magnetic resonance and inter-operative ultrasound images during prostate biopsy. Accurate registration ensures appropriate biopsy sampling of malignant prostate tissues and reduces the rate of re-biopsies. Therefore, we provide comparisons and experimental results for some landmark- and intensity-based registration methods: thin-plate splines, free-form deformation with B-splines. The primary contribution of this thesis is a new spline-based diffeomorphic registration framework for multimodal images. In this framework we ensure diffeomorphism of the thin-plate spline-based transformation by incorporating a set of non-linear polynomial functions. In order to ensure clinically meaningful deformations we also introduce the approximating thin-plate splines so that the solution is obtained by a joint-minimization of the surface similarities of the segmented prostate regions and the thin-plate spline bending energy. The method to establish point correspondences for the thin-plate spline-based registration is a geometric method based on prostate shape symmetry but a further improvement is suggested by computing the Bhattacharyya metric on shape-context based representation of the segmented prostate contours. The proposed deformable framework is computationally expensive and is not well-suited for registration of inter-operative images during prostate biopsy. Therefore, we further investigate upon an off-line learning procedure to learn the deformation parameters of a thin-plate spline from a training set of pre-operative magnetic resonance and its corresponding inter-operative ultrasound images and build deformation models by applying spectral clustering on the deformation pa-rameters. Linear estimations of these deformation models are then applied on a test set of inter-operative and pre-operative ultrasound and magnetic resonance images respectively. The problem of finding the pre-operative magnetic resonance image slice from a volume that matches the inter-operative ultrasound image has further motivated us to investigate on shape-based and image-based similarity measures and propose for slice-to-slice correspondence based on joint-maximization of the similarity measures.